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Digital transcriptome subtraction (DTS) is a bioinformatics method to detect the presence of novel pathogen transcripts through computational removal of the host sequences. DTS is the direct in silico analogue of the wet-lab approach representational difference analysis (RDA), and is made possible by unbiased high-throughput sequencing and the availability of a high-quality, annotated reference genome of the host. The method specifically examines the etiological agent of infectious diseases and is best known for discovering Merkel cell polyomavirus, the suspect causative agent in Merkel-cell carcinoma.[1]
Using computational subtraction to discover novel pathogens was first proposed in 2002 by Meyerson et al.[2] using human expressed sequence tag (EST) datasets. In a proof of principle experiment, Meyerson et al. demonstrated that it was a feasible approach using Epstein–Barr virus-infected lymphocytes in post-transplant lymphoproliferative disorder (PTLD).[3]
In 2007, the term "Digital Transcriptome Subtraction" was coined by the Chang-Moore group,[4] and was used to discover Merkel cell polymavirus in Merkel-cell carcinoma.[1]
Simultaneously to the MCV discovery, this approach was used to implicate a novel arenavirus as cause of fatality in a case where three patients died of similar illnesses shortly following organ transplantations from a single donor.[5]
After treatment with DNase I to eliminate human genomic DNA, total RNA is extracted from primary infected tissue. Messenger RNA is then purified using an oligo-dT column that binds to the poly-A tail, a signal specifically found on transcribed genes. Using random hexamers priming, reverse transcriptase (RT) convert all mRNA into cDNA and cloned into bacterial vectors. Bacteria, usually E. coli, are then transformed using the cDNA vectors and selected using a marker, the collection of transformed clones is the cDNA library. This generates a snap-shot of tissue mRNA that is stable and can be sequenced at a later stage.
The cDNA library must be sequenced to great depth (i.e. number of clones sequenced) in order to detect a theoretical rare pathogen sequence (Table 1), especially if the foreign sequence is novel. Chang-Moore recommend a sequencing depth of 200,000 transcripts or greater using multiple sequencing platforms.[1]
| % Viral | 5,000 clones | 10,000 clones | 20,000 clones | 50,000 clones |
|---|---|---|---|---|
| 0.001% | 4.9% | 9.5% | 18.1% | 39.3% |
| 0.01% | 39.3% | 32.2% | 86.5% | 99.3% |
| 0.02% | 63.2% | 86.5% | 98.2% | >99.995% |
| 0.03% | 77.7% | 95.5% | 99.8% | >99.995% |
| 0.04% | 86.5% | 98.2% | >99.995% | >99.995% |
| 0.1% | 99.3% | >99.995% | >99.995% | >99.995% |
Stringent quality control are then applied to the raw sequences to minimize false-positive results. The initial quality screen uses several general parameters to exclude ambiguous sequences, leaving behind a dataset of high-fidelity (Hi-Fi) reads.
Using MEGABLAST, Hi-Fi reads are then matched to sequences in annotated databases and any positive matches are then subtracted from the dataset. Minimum hit length for a positive match of human sequence is typically 30 consecutive identical bases, which equates to a BLAST score of 60; generally, the remaining sequence is BLAST again with less stringent parameters to allow for slight mismatches (1 in 20 nucleotide). The vast majority of sequences (>99%) should be removed from the dataset at this stage.
Subtracted sequences typically include:
After stringent rounds of subtraction, the remaining sequences are clustered into non-redundant contigs and aligned to known pathogen sequences using low-stringency parameters. As pathogen genomes mutates quickly, nucleotide-nucleotide alignments, or blastn[broken anchor], is usually uninformative as it is possible to have mutations at certain bases without changing the amino acid residue due to codon degeneracy. Matching the in silico translated protein sequences of all 6 open reading frames to the amino acid sequence to annotated proteins, or blastx[broken anchor], is the preferred alignment method as it increases the likelihood of identifying a novel pathogen by matching to a related strain/species.[5] Experimental extension of candidate sequences might also be used at this stage to maximize chances of a positive match.[6]
In cases where alignment to known pathogens is uninformative or ambiguous, contigs of candidate sequence can be used as templates for primer walking in primary infected tissue to generate the complete pathogen genome sequence.[1][5] As viral transcripts are exceedingly rare ratio tissue mRNA (10 transcripts in 1 million),[1] it is unlikely to generate a transcriptome based on the original candidate sequences alone due to low coverage.
Once a putative pathogen has been identified in the high-throughput sequencing data, it is imperative to validate the presence of pathogen in infected patients using more sensitive techniques, such as:
The primary application for DTS lies in identification of pathogenic viruses in cancer.[1][4] It can also be used to identify viral pathogens in non-cancer related disease.[5] Future clinical applications could include the use of DTS on a routine basis in individuals. DTS could also apply to agriculture, identifying pathogens that have an effect on output. Computation subtraction was already used in a metagenomics study that associated viral infection by IAPV with colony collapse disorder in honey bees.[7]
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