RNA-Seq and WES data

RNA-Seq and WES data of the analyzed cell lines (2 x 101 bp paired-end) were retrieved from Cancer Cell Line Encyclopedia (CCLE) via genetorrent from CGHub (https://cghub.ucsc.edu/software/downloads/GeneTorrent/3.8.7). In order to realign NGS data to virus sequences, the original downloaded bam files were sorted (samtools 0.1.19) and converted to fastq files (bedtools v2.21.0, BamToFastq). Reads were trimmed via fastq-mcf (ea-utils 1.1.2–686, fastq-mcf including homopolymer filtering and removing <Q20 flanking bases). Paired-end reads were joined where appropriate (ea-utils 1.1.2–686, fastq-join).

Virus genomes

For the specific detection of virus sequences in the NGS data sets joined and unmerged sequence reads were aligned via STAR (2.5.2a, < = 10 mismatches, > = 20 bp chimeric alignments) to a combined genomic reference of the human genome (GATK hg38) and the virus genomes simultaneously. The virus sequences used were either the reference sequences or sequence data of representative virus strains (Table 1). All mapped reads were merged (samtools merged, 0.1.19) and counted for each contig given including human chromosomes and viral sequences (samtools idxstats).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Viral genome reference used for detection of viral sequences in CCLE RNA-Seq and WES datasets.

https://doi.org/10.1371/journal.pone.0210404.t001

Virus detection via Taxonomer

Trimmed and/or untrimmed sequence data files in fastq format were adjusted to approximately five gigabyte (maximal file size recommended for Taxonomer) using the “head”-command (head–n 80000000 [filename.fastq] > [filename1.fastq]). This non-random selection of reads might have introduced a sequencing bias due to favoring the first tiles of the sequencing plate leading to different base calling qualities compared to the rest of the plate which could result in lower qualities and diminished mapping rate. As the aim of this study was to detect virus incidence rather than to quantitatively measure virus abundance, we applied the simple subsampling via “head”. Regarding untrimmed sequence files, this corresponded to 2.0 x 10⁷ reads or 8.0 x 10⁷ lines. These files were analyzed with Taxonomer, a k-mer-based interactive metagenomics sequence analysis tool accessed through a web interface on the IOBIO framework (Center for Genetic Discovery at the University of Utah, Salt Lake City, USA). The software classifies each NGS read to one of the categories human, bacteria, viruses, phages, fungi, PhiX (used as a control for Illumina sequencing runs), ambiguous, and unknown sequences. This procedure is called “binning” and compares the read sequences to various sequence databases optimized for rapid k-mer queries. Unlike the other categories, previously unclassified sequences which might originate from virus sequences are first translated into amino acid sequences by a six-frame translation. The resulting amino acid sequences are then aligned to viral protein sequences of the UniProt public databases. This subroutine is called “Protonomer” as distinct from the Taxonomer application. This additional step has the advantage that genetically variable viruses can be detected even if the nucleotide sequences do not fit applying the exact k-mer matching. This method also offers the opportunity to even detect novel viruses.

The program can be run in two different modes. The first one is the full analysis mode screening all reads of a given data set with a file size limitation of five gigabyte corresponding to approximately 20 million reads of ca. 100 bp in length. The second mode is a quick analysis screening in which only a subset of 200,000 reads is analyzed. Selected reads identified by Taxonomer as virus specific were verified by a BLASTn comparison [14] to the National Center for Biotechnology Information (NCBI) nucleotide collection (nr/nt) database using MegaBLAST with the default parameters.

Five data sets of cell lines infected with viruses (as shown by PCR) and 25 data sets of cell lines not infected with viruses (according to PCR analysis) were randomly selected and trimmed to remove poor quality and adaptor sequences using Trimmomatic v0.36 [15] and FastQC v0.11.5 for quality evaluation. The Trimmomatic tool was run for Phred +33 files and we chose a sliding window of four nucleotides and a mean Phred score (Q value) of greater than 30 (SLIDINGWINDOW:4:30) corresponding to a mean P error of 0.001 over the four nucleotides. Furthermore, each sequence had to be longer than 80 nucleotides (MINLEN:80).

PCR detection

Total nucleic acid was extracted from PBS-washed cell pellets of cell lines using the High Pure PCR Template Preparation kit (Roche, Mannheim, Germany) according to the recommendations of the manufacturer. The DNA of ca. 5 x 10⁶ viable cells was eluted in 200 µl dH₂O. The DNA concentration and purity was determined using a Nanodrop 1000 fluorometer (Peqlab, Erlangen, Germany). Approximately 200 to 500 ng of the isolated DNA were used for the PCR amplification.

Bovine polyomavirus (BPyV) detection of the SK-BR-3 cell line. The forward primer was described previously [16] (QB-F1-1: 5´-CTAGATCCTACCCTCAAGGGAAT-3´) whereas the reverse primer BPyV-rev was newly designed according to the NCBI reference sequence (NC_001442.1): 5´-CTGACCTCCTCAACCTGTTTATC-3´. The PCR product is 422 bp long. One U TaKaRa Taq HS polymerase (Takara Bio Europe, Saint-Germain-en-Laye, France) and the respective 10x buffer (containing 15 mM MgCl₂) were used in a 25 µl reaction mix containing 0.2 mM of each deoxynucleotide and 0.4 µM of each primer for the amplification. The hot start PCR was started with a Taq HS polymerase activating step at 94°C for 2 min. Subsequently, 35 cycles with denaturation steps at 94°C for 5 s, annealing steps at 57°C for 10 s, and amplification steps at 72°C for 20 s (plus 2 s extension for each cycle) were run. The amplified products were analyzed by agarose gel electrophoresis and visualized by ethidium bromide intercalation. The PCR product was then isolated from the reaction mix using a PCR product isolation kit (Qiagen, Hilden, Germany). For lack of a positive control for BPyV, the purified PCR product was sent away for sequencing (Eurofins Genomics, Munich, Germany). The DNA sequence was identified by a BLASTn alignment to the NCBI nucleotide (nr/nt) database (for parameters see paragraph on ´Virus detection via Taxonomer´).

HTLV-1 detection. A mixture of oligonucleotides was used for the PCR assay: forward SK110 (5´-CCC TAC AAT CCA ACC AGC TCM G-3´, 5´-CCT TAC AAT CCA ACC AGC TCA G-3´, and 5´-CCA TAC AAC CCC ACC AGC TCA G-3´); reverse SK111 (5´-GTG RTG GAT TTG CCA TCG GGT T-3´ and 5´-GTG GTG AAG CTG CCA TCG GGT T-3´). The PCR product is 182 bp long. The PCR was performed as described for the BPyV detection, but 30 cycles were run at 94°C denaturation temperature for 4 s, primer annealing at 60°C for 8 s, and amplification occurred at 72°C for 16 s plus 1 s extension for each cycle. The amplified products were identified by agarose gel electrophoresis and visualized by ethidium bromide intercalation. Genomic DNA of the HTLV-1 positive cell line MT-1 was used as a positive control.

Direct comparison of PCR-based virus detection and RNA-Seq data-set analysis

In previous studies, we described the detection of virus infections in human and non-human primate cell lines by PCR assays [10, 17]. To demonstrate the applicability of NGS data for the detection of viruses in cell culture samples, we screened all cell lines previously shown by PCR to be virus infected and whose RNA-Seq (n = 20) or WES (n = 5) data are publicly available in the CCLE database [18]. The RNA-Seq and WES data were aligned against the respective complete viral genomes available in GenBank as listed in Table 1. Additionally, we chose 111 RNA-Seq as well as 58 WES data sets of predominantly leukemia and lymphoma cell lines which were PCR-negative for the panel of tested viruses to assess the accuracy of the alignment results (Tables 2 and S1).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Number of RNA-Seq reads showing complete homology to viral reference sequences.

https://doi.org/10.1371/journal.pone.0210404.t002

As shown in Table 2, the sequencing depth of the RNA-Seq data sets varied between 2.6 x 10⁷ reads and 1.0 x 10⁸ reads with an average of 7.3 x 10⁷ reads. This overall number of sequenced reads usually allows the quantification of transcripts with low levels of expression [19]. As the aim of our RNA-Seq analysis is to qualitatively detect any viral gene expression and not the exact determination of differential expression levels, no further normalization procedures were done. On the other hand, a very high sequencing depth might lead to the formation of transcriptional noise. Besides the sequencing depth the type of RNA preparation (e.g. total RNA, mRNA, including small RNAs) and transcript length are important for the representation of sequences within the RNA-Seq data sets. Most of the protein coding mRNAs are well represented in the reads of moderate sequencing depth (5 x 10⁷ reads) whereas small RNA species are less represented in those data sets, but their relative abundance increases with ultra-high-throughput sequencing (1 x 10⁸ reads) [20]. Viruses affect the physiology of infected cells, regardless of the infection status. Thus, the viral genome is at least in part transcribed to produce regulatory active RNAs (e.g. miRNAs, lncRNAs) or mRNAs for protein expression. All these RNAs are polyadenylated, isolated with poly-A+ RNA enrichment and should be well represented in the data sets. Moreover, as we already had obtained data regarding the virus infection status of the investigated cell lines by PCR, we were able to use those results as valid reference to evaluate the results of the NGS data analyses.

Regarding the cell lines positive for virus infections as shown by PCR all virus specific reads ranged from approximately 2,700 reads (HBV of HEP-3B), corresponding to 0.00382% of all reads and up to 1.2 x 10⁷ reads (XMRV of 22RV1) corresponding to as many as 14% of all reads. The median number of virus specific reads is 196,535 at a median total reads number of 7.8 x 10⁷. This corresponds to 0.25% of the total reads number. No virus specific reads were detected for the vast majority of the cell lines for HBV, HCV, HIV-1 and -2, and HPV infections. Overall, 17 cell lines showed one to four HBV specific reads, 25 cell lines up to three HCV reads, two cell lines one and 11 HIV-1 reads, two cell lines with one HIV-2 read each, one cell line with one HPV read, and two cell lines with one and 16 HTLV-2 reads. HHV-8 specific reads of 39 cell lines were predominantly also very low, but with single samples showing up to 27 reads.

Regarding HTLV-1, 25 cell lines showed a notable number of up to 166 (average: 53) aligned reads. Although the PCR positive cell lines showed a minimum read number that was more than fifteenfold, we have further analyzed the individual read sequences for HTLV-1 specificity. The qualities of the reads are good or even excellent according to the Phred-Score values of each nucleotide of the sequences. A subsequent BLASTn analysis of the reads revealed 98–100% nucleotide identity for HTLV-1 sequences listed in the public databases. Furthermore, the sequences represent regions of the complete proviral HTLV-1 genome. Table 3 exemplifies alignments of a few HTLV-1 specific reads of the MHH-CALL-4 cell line. Thus, we conclude that the reads are indeed present in the samples and that they were correctly analyzed and assigned.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. HTLV-1 reads aligned to HTLV-1 sequences of the NCBI sequence data base.

https://doi.org/10.1371/journal.pone.0210404.t003

We then tried to validate the results with a verification of the initial PCR analyses. If the cell line is infected with HTLV-1, each of the cells in the culture should contain at least one copy of the virus as provirus integrated into the genome. If only a few cells express RNA as detected by RNA-Seq analysis, PCR of the genomic DNA of the cell line should be able to amplify the HTLV-1 sequence. However, no HTLV-1 specific sequences were amplified confirming the initial test results. Moreover, we performed RNA-Seq of our own HPB-ALL cells (DSMZ ACC 483) which are listed with 146 HTLV-1 reads in the CCLE data-set. Analyzing the reads with the same method as the CCLE RNA-Seq data revealed no HTLV-1 specific sequences.

While arranging the cell lines according to their accession numbers, we found that 24 apparently HTLV-1 containing cell lines clustered in one single group from G28530.MUTZ-5 to G28888.Hep_3B2.1–7 (Table 4). Only G27380.EOL-1 with a single HTLV-1 specific read was located outside this cluster. Due to their origin and cell type, it seems to be very unlikely that most of the HTLV-positive cell lines are in fact infected with HTLV-1 ab initio. Thus, we conclude that the retrovirus-specific mRNAs were caused by a contamination during RNA extraction or library preparation, assuming that the cell lines were analyzed in the same laboratory and that the RNAs were prepared with the same extraction method. This implication is supported by the observation that the magnitude orders of other viruses also show a significant clustering among the other low level virus-specific reads. This is obvious for 15 cell lines regarding HHV-8 which arrange similarly as HTLV-1, but with 3 to 27 HHV-8 specific reads per cell line, forming a consistent set of cell lines with low level reads from G27318.DEL to G27390.CMK. A third clear clustering concerns SMRV detection. Forty-four cell lines display contamination caused reads in the range of 28 to 172 specific reads interrupted by one cell line with no specific reads (KM-H2) and one SMRV positive cell line (NAMALWA) (Table 4). Although almost all cell lines display virus-specific reads for MLV, interestingly, similar magnitudes of reads cluster in separate sets (data not shown).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. HTLV-1, HHV-8, and SMRV specific read numbers of cell lines ordered by CCLE file names.

https://doi.org/10.1371/journal.pone.0210404.t004

EBV is the only virus of which the low number reads are almost constantly distributed over all cell lines. Ninety-six cell lines revealed EBV specific reads of which 85 cell lines showed read numbers of one to a maximum of 38 reads in the cell line DEL. The other cell lines were EBV positive according to PCR analysis with read numbers between 33,388 (NAMALWA) and 575,192 (EB-1) demonstrating the enormous difference between true positive samples and samples with viral contamination caused reads.

Only the cell line DOHH-2 revealed a notably higher, although compared to PCR positive samples still very low number of EBV specific reads (n = 724). We have previously shown by fluorescence in situ hybridization that the DOHH-2 cell line consisted of two clones: most of the cells were clearly EBV negative, whereas a few cells contained several EBV episomes within the nucleus [17]. Single cell cloning was performed to separate the EBV-negative from the EBV-positive cells. The original publication describing the establishment of the DOHH-2 stated that the cell line was negative for the Epstein-Barr virus nuclear antigen type 1 (EBNA-1) [21]. The relatively low number of EBV-specific reads compared to all other EBV positive cell lines can be attributed to the original mixed culture containing a low portion of EBV positive cells. This result is in concordance with the findings of Cao et al. [22] who also determined low level expression, but significantly above the contamination caused level.

Regarding the animal retroviruses MLV, XMRV, and SMRV, a large number of cell lines revealed virus specific sequence reads. In general, MLV and XMRV showed a higher magnitude of the contamination caused reads per cell line (up to 850 for KI-JK) compared to the low level reads of other investigated viruses. Moreover, specific reads were found in a high proportion of the cell lines (116 low level positive cell lines of 124 cell lines with XMLV reads and 111 low level of 119 cell lines with XMRV reads). The similar number of cell lines with MLV and XMRV specific reads is attributed to the high relatedness of the genomes because XMRV is in fact a hybrid of two different XMLV strains [23]. Regarding SMRV, 47 cell lines exhibit of up to 172 specific reads for that virus. In contrast to those numbers the clearly SMRV positive NAMALWA cell line exhibits almost 3 x 10⁶ specific reads. Although not as striking as in the case of HTLV-1, all these reportedly positive cell lines cluster in sets with higher or lower numbers of virus specific reads. In our opinion, all these examples support the explanation that the low count virus specific reads are artifacts which can be attributed to contaminations in the course of RNA extraction or during further processing steps. For instance, multiple cell line sequencings performed on the same sequencing run (sample multiplexing) may lead to index misassignment (cross-talk) due to inappropriately matched indices of the library pool to sequence reads which were derived from different samples in the pool. This can be virtually eliminated using dual-matched indexed adapters containing an additional “Unique Molecular Index” (UMI) appended to the 3´ end of the i7 index for molecule barcoding [24].

Several studies have investigated the assignment of unmapped reads in NGS data. According to Tae et al. 0.13% of the reads in a subset of 150 genomes of the 1000 Genomes Project showed similarities to non-human genomes [25]. Although they investigated WGS and WES, they found that different sequencing centers had specific signatures of contaminating genomes as ´time stamps´. Strong et al. describe a similar finding regarding RNA-Seq data sets from The Cancer Genome Atlas (TCGA) [26]. They analyzed 244 different specimens from different sources and from different specimen types and found average numbers ranging from 1406 reads per million human mapped reads (RPMHs) to 11,106 RPMHs with taxa-specific differences across centers. Furthermore, identical cell lines analyzed in separate studies showed differences in bacterial read profiles. R.W. Lusk claims that “contamination must be considered a potential source of signals of exogenous species in sequencing data, even if these signals are replicated in independent experiments, vary across conditions, or indicate a species which seems a priori unlikely to contaminate” [27]. Another indication that contaminations might be prevalent in nucleotide samples for NGS projects was shown in our previous study detecting XMLV contamination in human cell lines. Four out of 23 PCR-positive cell lines turned out to be false positive due to previously unrecognized DNA contamination with mouse DNA introducing the endogenous XMLV sequences into the sample. The contamination was verified by the amplification of intracisternal A particles (IAP) which are present at ca. 1,000 copies per haploid mouse genome. The low level contaminations occurred when mouse DNA was extracted simultaneously with or previously to the human DNA [10]. Contamination of NGS samples with mouse DNA or RNA might be an explanation for the widespread occurrence of the retroviral sequences in the NGS data sets.

Taken together, virus infections of cell lines can easily and with high specificity and sensitivity be detected in RNA-Seq data sets. However, low numbers of virus specific reads are likely to be the result of contaminating nucleic acids rather than an indication of viral infections in cell lines. The identification of contaminating RNA or DNA appears to be difficult because minimal amounts of the nucleic acids result in the generation of the few reads and they can only be detected if they are not a genuine part of the human transcriptome and can be mapped as in the current study. If the contamination is of human origin, identification appears to be impossible. Performing RNA preparations and sequencing reactions in parallel with control samples known to be free of contaminations and subsequent alignment against an artificial chromosome of the different virus sequences may give evidence of a contamination. A strict separation of RNA preparation for NGS and other molecular biology experiments seems to be mandatory. In the current study we were able to identify contaminated cell lines by means of verification with an independent PCR assay and the arrangement of numerous data sets. Defining thresholds for individual viruses appears to be difficult but for cell lines read numbers of up to 1,000 might be attributed to contaminations. But this could be different for primary tumor samples or other cell samples. However, the HBV infected cell line HEP-3B exhibited less viral reads (n = 2,695) than the DU-145 cell line which is PCR negative for MLV but shows 3,447 MLV reads. The RNA extraction and library preparation of cell lines with such high numbers of virus specific reads should be repeated, if possible, or a possible infection should be verified with another method.

Direct comparison of PCR-based virus detection and WES data set analysis

WES data are available for a number of cell lines in the CCLE database. We have analyzed the sequences of 62 data sets of continuous cell lines in an identical manner as the RNA-Seq data sets. The total read numbers of the samples were in the same range as the RNA-Seq data: from approximately 3.0 x 10⁷ reads to about 1.02 x 10⁷ reads, representing a high coverage of the exome. As WES reads are generated with exon specific primers, virus specific sequences are generally off-target sequences and part of the sequences unmapped to the human genome. Therefore, for WES data sets virus specific sequences are generally expected at a lower proportion of virus specific reads relative to the total reads compared to a respective RNA-Seq data set. As shown in S1 Table, we analyzed the data sets of 62 cell lines matching RNA-Seq data sets. Of those, six cell lines were previously shown by PCR analysis to be virus infected. Of those virus infected cell lines only the HBV infection of the HEP-3B cell line was not detected. The HBV genome of the HEP-3B cell line is only about 2 kb in size. This sequence might be too short for a detection as an untargeted sequence even at high coverage. This might also be true for other viruses with small genomes such as parvoviruses, circoviruses and segmented viruses where individual segments may be only a few kilobases in length.

The number of virus specific reads detected in the data sets of the infected cell lines is substantially lower in WES compared to RNA-Seq results. RNA-Seq reveals ca. 30 to 91 times more EBV reads (4,583 versus 140,047 reads for CI-1 and 365 versus 33,388 reads for NAMALWA, normalized to the total number of reads) and even about 93,000 times more XMRV reads (129 versus 1.2 x 10⁷ reads for 22RV-1, normalized to the total number of reads). The least number of 34 virus specific reads was obtained for the cell line NAMALWA for SMRV (versus 3 x 10⁶ regarding RNA-Seq) and the cell line CI-1 exhibited the highest number of 2,144 virus specific reads for EBV. Except for HEP-3B, all cell lines previously found to be virus infected were also positive (4 x EBV, 1 x XMRV, 1 x SMRV) by sequence analysis. This clearly demonstrates that RNA-Seq data are more powerful regarding sensitivity, clarity and reliability for the detection of viruses in cell cultures than WES data.

On the other hand, the results of WES data were more specific. Except for 11 cell lines matching EBV unspecifically with 16 or less reads, only one unspecific HCV alignment was detected by this method (OCI-LY19). No other unspecific reads were listed.

Taken together, WES data sets are less qualified for virus detection in cell lines than RNA-Seq data sets. The detection accuracy highly depends on the quality of the sequencing procedure: highly specific annealing conditions for the sequencing primers decreases the number of off-target reads that do not map to the human genome and thus decreases the number of virus specific reads. Regarding publicly available data sets from different sequencing centers the quality standards might be different and the results might not always be directly comparable.

Taxonomer

To address the general infection status of cell lines regarding viruses, we applied the publicly accessible analysis tool Taxonomer of IDbyDNA.

As mentioned before, we have previously analyzed all human and non-human primate cell lines for specific viral pathogens by PCR. Other viruses found to be present in cell cultures (e.g. MLV, XMRV, SMRV) were investigated for the characterization of the cell lines. However, apart from those viruses, we have never been certain whether or not other viruses were present in the cell lines originating from the patient or the original host or whether they might have been introduced later during cell cultivation (e.g. by FBS or other cell culture supplements or from specific treatments of the cells) [28].

As shown in the section on the evaluation of the RNA-Seq data for specific viruses in comparison with the findings using PCR detection, all contaminating viruses could be detected applying the NGS data sets. We chose 301 CCLE RNA-Seq data sets for a screening with the Taxonomer tool applying the quick analysis mode (S2 Table). All of the cell lines are concurrently part of the Leibniz Institute DSMZ cell lines bank and had been tested by PCR for virus infections.

In one approach a selection of 30 out of the 301 RNA-Seq data sets were trimmed to eliminate the low-quality sequences applying the Trimmomatic tool developed by Bolger et al. [15]. We chose a sliding window of 4 nucleotides and a mean Phred score (Q value) of greater than 30 (SLIDINGWINDOW:4:30) corresponding to a mean P error of 0.001 over the four nucleotides. Furthermore, each sequence had to be longer than 80 nucleotides (MINLEN:80).

Taking the BL-70 cell line as an example, applying this process to the first 20 million sequences of the RNA-Seq data set, the number of reads complying with the mentioned criteria was reduced to 6,076,955 reads (30.38%). The following Taxonomer analysis revealed three virus specific reads after binning and two reads after the additional Protonomer analysis (Fig 1B). The unclassified read was identified as XMLV. Of the two reads classified by Protonomer one was also identified as XMLV and the second classified read aligned to a human endogenous retrovirus K (HERV-K). Taking the number of XMLV reads determined by the virus genome specific analysis as a basis (447 out of 7.73 x 10⁷ reads, as mentioned in Table 2) and assuming an equal distribution of the XMLV sequences in the data set, two XMLV specific reads is exactly the number to be expected in the quick analysis. In fact, the sequences assigned to XMLV were two of the reads determined by the alignment of the XMLV genome sequences. Thus, the XMLV hits were correctly assigned by Taxonomer, although, as discussed before, we conclude that the detected reads originate from a contamination of the RNA sample.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Virus detection in the cell line BL-70 applying the Taxonomer bioinformatics tool before and after trimming.

Shown are analysis outputs of quick analyses of 213,249 reads. Of those, 125,509 reads (59%) were classified to the human, bacterial, and viral bins (numbers in barackets). A) The table lists all reads categorized to the viral bin before trimming. The reads with a taxonomic level higher than 1 are depicted in the circular diagram. B) The table lists the remaining viral reads after trimming the reads using the Trimmomatic tool (SLIDINGWINDOW:4:30; MINLEN:80) and the two classified viral reads in the circle.

https://doi.org/10.1371/journal.pone.0210404.g001

Of the 30 cell lines selected for read trimming, four were positive for virus infections as confirmed by the other detection methods before. Table 5 shows that number of reads with viral sequences were almost constant before and after trimming regarding those virus positive cell lines (22RV1, DAUDI, DEL, MEC-1), indicating that most of the confirmed virus positive reads were specific and of good quality. On the other hand, regarding 14 cell lines with viral reads in the range of 10 to 50 before trimming none of the viral reads survived the trimming procedure and no further previously unconsidered reads were appended. The remaining cell lines perform similarly as the beforehand as example mentioned BL-70 lying in the range of one to five virus reads after trimming. Only cell line DU-145 revealed nine viral reads, all identified as retroviruses and eight of them were further classified as gammaretroviruses. The direct screening of the DU-145 reads for XMLV and XMRV complete genomes exhibits a relatively high number of XMRV reads (n = 3,447; see Table 2). Given a total read number of 3.35 x 10⁷, this would result in approximately one XMRV read per 10,000 reads. Hence, even about 20 reads might have been expected in the Taxonomer quick analysis of 2 x 10⁵ reads. The range of XMLV (XMRV) positive cell lines show read numbers of 261 to almost 13,000 classified reads, which is at least 30 times more than the contamination caused counts. Most of the viral reads after trimming were classified as XMLV (XMRV), but also single reads belonged to Feline sarcoma virus, Paramyxovirinae, and Pandoravirus salinus. None of the latter viruses was ever described to affect human continuous cell cultures. As discussed before, it is likely that these hits result from amplifications inherent to the NGS method or might be a result of homologous sequences between those viruses and the murine retroviruses.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 5. Comparison of read numbers assigned to the viruses and bacteria bins before and after trimming the RNA seq data sets.

https://doi.org/10.1371/journal.pone.0210404.t005

In the approach using the untrimmed RNA-Seq data sets for the Taxonomer tool the program detected several virus sequences of diverse virus species in almost all cell lines which had not been detected before. Regarding the aforementioned BL-70 data set, Taxonomer´s quick analysis (“Binner”) categorized 213,249 reads to the major taxonomic categories human (191,676 reads), bacterial (6,170 reads), and viral (43 reads), as well as 1,410 reads to an “ambiguous” bin (sequences that can be categorized to more than one bin) and 13,950 reads to a so called “unknown” bin (sequences that could not be assigned to one of the other bins) (Fig 1). The reads within a bin were further classified according to different features of the sequences: (1) 119,615 human reads deriving from mRNAs, (2) 5,853 bacterial reads deriving from 16S rRNAs, and (3) 41 viral reads identified by the Protonomer module based on the protein database UniProt. Regarding the viral reads, 27 were assigned to the virus species or genera listed in Table 6. A graphic representation of the distribution of the virus species to the different viral taxa is shown in Fig 1A.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 6. BL-70 RNA-Seq reads from the Taxonomer virus bin assigned to virus species or genera.

https://doi.org/10.1371/journal.pone.0210404.t006

As shown in S2 Table, between 0 and 59 classified reads were assigned to up to 18 different virus families for the individual non-infected cell lines. To evaluate the virus associated reads, we analyzed the sequences in detail and examined the quality scores of the reads. We chose the cell line MHH-CALL-4 which was shown to reportedly carry HTLV-1 specific sequences by direct alignment (see section on direct comparison of PCR-based virus detection and RNA-Seq data set analysis). As shown in S3 Table, most of the “viral” reads of the cell line MHH-CALL-4 exhibited at least partially a low sequencing quality represented by a significant portion of “#” symbols, which typify a P error of 0.63096. Almost all sequences showed no significant similarity performing a BLASTn analysis and two of the partially low or completely low-quality reads exhibited at least partial similarity to two human genes. On the other hand, three of partially or completely good quality viral reads revealed almost complete homology to XMLV sequences.

The numbers of classified reads assigned to viruses in general and the number of identified virus families they were attributed to are summarized in S2 Table. The table shows that the virus infected cell lines can readily be identified by the highly elevated number of virus specific reads compared to the cell lines shown to be virus negative by PCR. On the other hand, similar numbers of classified virus reads and virus families indicate that only a few reads were assigned to the individual virus families and that they are most likely a result of low-quality reads or contaminations.

To exclude that the poor-quality reads of an RNA-Seq analysis are accumulated in the first part of the data file, we used the full analysis mode of Taxonomer screening 2 x 10⁷ reads. The cell line CADO-ES-1 with 50 reads assigned to 12 different virus families revealed one of the highest read number for virus negative cell lines employing the quick analysis mode for 213,000 reads. The full analysis revealed 4,257 reads assigned to 28 virus families. The vast majority of the reads were assigned to the virus families identified by the quick analysis. This demonstrates that the quick analysis represents well the result of an almost hundredfold number of reads. The number of hits of the quick analysis varies by +10% relative to the full analysis of the CADO-ES-1 cell line. Similar results were obtained for the cell lines DOHH-2 (-6%) and HCC-827 (+12%). The quick analysis of the cell lines HCC-827 and KASUMI-1 showed a 40% and 46% higher number of virus specific reads compared to the full analysis, respectively. On the other hand, the cell lines HCT-15, KASUMI-6, KYSE-30, and MHH-CALL-4 revealed lower virus specific hits of -49%, -38%, -68%, and -43%, respectively, for the quick analysis. Taking into account, that the virus specific hits of those cell lines were in the range between 6 (HCT-15 and KASUMI-6) and 50 (CADO-ES-1), all viruses would have been detected with the full set of reads (100x more reads). We also compared the quick analysis of the EBV positive cell line CI-1 exhibiting an intermediate number of virus specific reads with its full analysis. The quick mode classified 193 reads as viruses whereas the full analysis mode assigned 18,708 reads as viruses. This corresponds to +2.6% with respect to the quick analysis. The results demonstrate that the virus specific reads are largely evenly distributed in the sequencing files.

The cell line Hep-3B2.1–7 exhibits similar numbers of virus specific reads and virus families. Taxonomer determined twelve Hepadnaviridae of which ten were assigned to HBV in cell line Hep-3B2.1–7 beside 19 reads of 12 other genera in 211,000 analyzed reads. Trimming the reads applying a sliding window of four with a mean phred score of 30 and a minimal length of 80 bp ended up in nine HBV specific reads and no additional virus specific reads (Fig 2A and 2B). Extrapolating the 12 HBV specific reads to the total number of 71 million reads results in 4,050 HBV specific reads. This number is in the range of what was detected by the direct genome alignment (n = 2,695) and confirms the relatively low transcription rate of HBV genes in comparison to other virus contaminations. As the HBV infection was verified previously by PCR this relatively low number of virus specific reads represents an authentic infection and needs to be demarcated from the other virus detections with similar or even more virus specific read numbers which can be traced back to sample contaminations. But concerning HBV, only 16 further cell lines revealed HBV specific alignments for only one to four reads. This indicates that sample contamination with HBV sequences is unlikely. As shown in Table 2, the same is true for HHV-8, HPV, HIV-1 and -2, HTLV-2 and HCV, whereas EBV, XMLV (incl. XMRV), SMRV and HTLV-1 are prone to sample contaminations.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Virus detection in the hepatitis B positive cell line Hep_3B2.1–7 applying the Taxonomer bioinformatics tool before and after trimming.

Shown are analysis outputs of quick analyses of 210,769 reads. Of those, 125,462 reads (60%) were classified within the human, bacterial, and viral bins (numbers in brackets). A) The table lists all reads categorized to the viral bin before trimming. The reads with a taxonomic level higher than 1 are depicted in the circular diagram. B) The table lists the remaining viral reads after trimming the reads using the Trimmomatic tool (SLIDINGWINDOW:4:30; MINLEN:80) and the nine consistently classified HBV reads in the circle.

https://doi.org/10.1371/journal.pone.0210404.g002

Concerning the virus infected cell lines, the results agree well with the results of the virus specific determinations of the RNA-seq data as well as the PCR results. During the course of the study we found one virus infection that had not been detected before: the SK-BR-3 revealed 163 reads of BPyV. This infection was verified by a BPyV specific PCR assay (Fig 3).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Detection of BPyV by PCR.

Shown is an ethidium bromide-stained gel containing the reaction products following PCR amplification with the primers QB-F1-1 and BPyV-rev. A product of 421 bp was obtained. Genomic DNA of the BPyV positive cell line SK-BR-3 and of the BPyV negative human liver cell line SNU-886 were used for the detection. The PCR product of SK-BR-3 was subsequently sequenced and aligned to BPyV sequences of the NCBI nucleotide database showing complete homology.

https://doi.org/10.1371/journal.pone.0210404.g003

BPyV was first detected in simian kidney cell lines and later shown to be of bovine origin [29]. The virus was transmitted from the fetal bovine serum to the cells. Due to the close relation of apes and man a transmission from FBS to human cell lines appears to be possible. Schuurman et al. determined an infection rate of more than 70% in different sera and that all PCR-positive sera contained infectious viruses [30]. Like other polyomaviruses (e.g. SV40) BPyV exhibits a large-T antigen which is responsible for the transforming properties of the virus. However, given the high rate of infected serum batches and the capability of the virus to infect bovine and primate cells it remains unclear why only one cell line was found to be positive for the virus. Furthermore, SK-BR-3 was established from breast carcinoma cells and not from kidney cells from which BPyV was usually isolated. Rare modified BPyV might be responsible for the productive, naturally occurring transmission from one host mammal to another. Further RNA-Seq or PCR studies will probably uncover some more infected cell lines.

Another bovine virus frequently detected in FBS (47%) is the bovine viral diarrhea virus (BVDV) [31]. This RNA virus belonging to the Flaviviruses can be detected in many cell lines of cloven-hoofed animal origin (e.g. FLK-BLV, LAT, EBL, MDBK) (determined by RT-PCR, results not shown). Cell lines from diverse animal species and human origin could be infected [32, 33]. But until now, no human cells lines were found to be positive for BVDV.

The results of the present study demonstrate that the methods used agree very well and that RNA-Seq data sets can conveniently be used for the detection of viruses in cell lines. Utmost attention should be paid during NGS laboratory preparation and bioinformatics analysis of RNA-Seq data sets, particularly in regards to: 1) RNA extraction and library construction in designated hoods/areas where proper cleaning procedures are in place to minimize cross-contamination, 2) quality filtering procedures for removal of primers/adapters and low quality bases. The latter point is particularly important for the Taxonomer analysis tool because all sequences are compared to several sequence databases relevant for human samples infected with microbiological agents. This also includes the protein data bases of viruses (UniProt). Taking into account that a six-frame translation is performed for the alignment with protein sequences, the poor-quality sequences may yield a number of false positive results. However, when 200,000 reads are analyzed those reads produce only very few hits per assigned virus family compared to hundreds of reads assigned to a single virus family regarding true positive samples. Nevertheless, filtering poor-quality reads of an RNA-Seq analysis by specific data processing tools (e.g. Trimmomatic) is recommended. Furthermore, dual-matched indexed adapters can be used to eliminate index misassignment when massively parallel sequencing is performed. If the handicaps of contamination and poor-quality reads are considered and carefully handled, all detection methods are well suited for the risk assessment of cells. The Taxonomer results show that the panel of viruses used for characterization of cell lines covers the vast majority of virus contamination. Furthermore, no “new” viruses that were previously not considered to be present in cell cultures could be identified.