- Technical advance
- Open Access
- Open Peer Review
Identification of microbial DNA in human cancer
© Duncan et al; licensee BioMed Central Ltd. 2009
- Received: 28 January 2009
- Accepted: 08 May 2009
- Published: 08 May 2009
Microorganisms have been associated with many types of human diseases; however, a significant number of clinically important microbial pathogens remain to be discovered.
We have developed a genome-wide approach, called Digital Karyotyping Microbe Identification (DK-MICROBE), to identify genomic DNA of bacteria and viruses in human disease tissues. This method involves the generation of an experimental DNA tag library through Digital Karyotyping (DK) followed by analysis of the tag sequences for the presence of microbial DNA content using a compiled microbial DNA virtual tag library.
To validate this technology and to identify pathogens that may be associated with human cancer pathogenesis, we used DK-MICROBE to determine the presence of microbial DNA in 58 human tumor samples, including brain, ovarian, and colorectal cancers. We detected DNA from Human herpesvirus 6 (HHV-6) in a DK library of a colorectal cancer liver metastasis and in normal tissue from the same patient.
DK-MICROBE can identify previously unknown infectious agents in human tumors, and is now available for further applications for the identification of pathogen DNA in human cancer and other diseases.
- Epstein Barr Virus
- Merkel Cell Carcinoma
- Representational Difference Analysis
- Microbial Sequence
- Exogenous Sequence
Pathogens are involved in a variety of human diseases. Of particular interest are emerging infections and idiopathic chronic diseases, including cancer . Nevertheless, clinically important microbial pathogens in human disease are likely to be under-recognized [2, 3]. Infectious agents have been identified as pathogens in several human tumors, accounting for approximately 20% of human cancers worldwide . However, this proportion accounts for only a few known viral or bacterial agents. Emerging data suggests the role of additional unknown microbes in tumorigenesis . Detection of these infectious agents could provide novel approaches to the prevention, diagnosis and therapy of human cancer.
In the search for the presence of pathogenic DNA in human disease tissues, two kinds of approaches, candidate-based and subtractive, have been previously used. Consensus polymerase chain reaction (PCR) [5–7] and newer DNA microarray-based screening [8–11] methods have been used for candidate-based foreign sequence identification. Candidate screens employing PCR-based techniques have been successfully used for identification and typing of HPV in cervical cancer . DNA microarrays composed of oligonucleotides corresponding to conserved sequences of multiple viruses have been applied to identify a new xenotropic murine leukemia virus-related virus in human prostate tumor cells . Subtractive methods, including representational difference analysis (RDA) , computational subtraction [14–16], and digital transcript subtraction , have been used to filter sequence data to identify non-human sequences. RDA has been used in the identification of herpesvirus in Kaposi's sarcoma , and a method based on long serial analysis of gene expression (LongSAGE) [19, 20], called digital transcriptome subtraction (DTS), has been used to identify a new polyomavirus in Merkel cell carcinoma .
While these methods have been successful in microbe identification, current techniques have limitations. Candidate-based approaches only confirm the presence of known viruses or bacteria . In addition, varying mechanisms of infection confound the identification of foreign transcript sequences, as the agent may remain in a latent state for years in the host cell without being detected by gene expression methods.
To overcome these limitations, we developed a pathogen discovery approach that applies computational subtraction to Digital Karyotyping (DK). DK identifies and enumerates short sequence tags to provide a comprehensive view of the genomic content of any DNA sample [22, 23]. Generally, experimental tags obtained from a DK library are matched to a virtual tag library derived from the human genome. Although the vast majority of tags are identical to the predicted virtual tags, there are inevitably experimental tags which do not match any human sequence. These sequences originate from a number of sources, including unpublished human sequences, tag site polymorphisms, sequencing errors, or foreign DNA sequences that are not present in the normal human genome.
We developed DK-MICROBE to quantitatively evaluate DK sequences that do not match the human genome and which may be derived from microbial genomic DNA. We verified the sensitivity and specificity of this approach by studying Epstein Barr virus (EBV)-infected human lymphoblastoid cells and murine retrovirus infected tumor xenografts. We then applied this technique to analyze brain, colorectal and ovarian tumors for viral and bacterial genome sequences.
The DK-MICROBE approach was designed for screening of microbial DNA in cancer tissues. Brain tumor tissue samples were obtained from the Preston Robert Tisch Brain Tumor Center Biorepository at Duke University Medical Center by an IRB-approved protocol. Frozen sections were made from each tumor sample and examined by light microscopy by a board-certified neuropathologist to ensure that more than 95% of the section consisted of tumor cells.
Colorectal cancer DNA samples used to make Digital Karyotyping libraries were derived from liver metastases of different patients. Two of the samples were low-passage cell lines established from liver metastasis, while seven samples were immunopurified from liver metastases using the BerEP4 antibody as previously described . Additional samples used for qPCR confirmation were low-passage xenografts established from liver metastases. Normal DNA samples were obtained from peripheral blood or adjacent normal liver.
Five of the ovarian cancer samples are from patients with high-grade ovarian serous carcinoma. Two of the samples are from cell lines purchased through ATCC. All samples were obtained in accordance with the Health Insurance Portability and Accountability Act (HIPAA).
DK-MICROBE experimental library generation
The initial aspects of performing DK-MICROBE are based on Digital Karyotyping (DK) . Briefly, genomic DNA is cleaved with the mapping enzyme SacI, which has a 6-bp recognition sequence that is predicted to cut genomic DNA into several hundred thousand pieces that are on average < 10 kb. Biotinylated linkers are ligated to the DNA molecules and then digested with the fragmenting enzyme NlaIII, which has a 4-bp recognition site. DNA fragments containing biotinlylated linkers are separated by using streptavidin-coated magnetic beads and are ligated to new linkers containing a class II MmeI site. The fragments are then cleaved by MmeI, releasing 21-bp tags. Isolated tags are self-ligated to form di-tags, PCR-amplified en masse, concatenated, cloned, and sequenced. The experimental tags, adjacent to the NlaIII fragmenting enzyme (CATG) sites closest to SacI mapping enzyme sites, are computationally extracted from sequence data.
We generated 31 brain tumor, 10 colorectal cancer metastasis, and 7 ovarian cancer DK libraries in our laboratories, and analyzed 10 brain libraries from the Cancer Genome Anatomy Project http://cgap.nci.nih.gov/SAGE/DKViewHome.
DK-MICROBE microbial virtual tag library
Generation of an extensive and representative microbial virtual tag library is essential for implementing the DK-MICROBE approach. We have assembled a viral and bacterial sequence database, which includes all of the complete bacterial http://ftpgib.genes.nig.ac.jp and viral ftp://ftp.ncbi.nih.gov/refseq/release/viral/ genomes of the NCBI Reference Sequence (RefSeq) collection as of May 9, 2008. The NCBI RefSeq database is a curated, non-redundant collection of reference sequences for each organism . Using this database, representative tag sequences were computationally extracted. The resulting tag collection contained 21 bp sequences at each NlaIII site closest to SacI sites for all bacterial or viral genomes in the NCBI RefSeq collection. Comparison of predicted tag sites from microbial and human genomes identified minimal overlap, as only 1,266 tags were present in both reference libraries, many of which correspond to common repetitive elements. This suggests that most 21 bp tags contain sufficient information to distinguish tags as distinctly human or microbial in origin. The tags which match both human and microbial genomes were excluded from the DK-MICROBE virtual tag library for use in further steps of these analyses. The filtering process removed all tags predicted in the human genome sequence (Build 36, March 24, 2008). A total number of 664,413 microbial virtual tags were obtained in this manner. This microbial virtual tag library (Additional files 1, 2 and 3) allowed us to quantitatively identify viral or bacterial genomes present in the analyzed samples by digitally matching experimentally isolated tags to the virtual tags of the reference library.
DK-MICROBE bioinformatic analyses
Seventeen bp tag sequences (adjacent to NlaIII sites) were extracted and enumerated. These experimental tags were linked to the microbial virtual tag library, and only tags matching microbial sequences perfectly were further considered. All DK tumor libraries were screened for the presence of exogenous DNA from viruses or microorganisms. Foreign genome presence or absence as indicated by DK-MICROBE was validated by PCR and real-time qPCR. Selected positive samples were verified by using direct nucleic acid sequencing.
DK-MICROBE web interface
We have generated a web site that is part of the Cancer Genome Anatomy Project (CGAP) which allows users to easily upload and analyze experimental DK-MICROBE tags for microbial DNA content. A search feature for tags against microbial genome sequences is available at http://cgap.nci.nih.gov/SAGE/DKMicrobe. Upon uploading a set of 17 base-pair Digital Karyotyping tags, the DK-MICROBE tool searches for exact matches to virtual tags in the genomes of 2,565 bacterial and viral genomes. DK-MICROBE utilizes the microbial virtual tag library described and will be updated to reflect new microbial genome sequences as they become available.
Principles of DK-MICROBE
First, the DK-MICROBE approach utilizes the previously described DK protocol for generation of short sequence tags from genomic loci [22, 25]. Isolated total genomic DNA is sequentially digested with mapping enzyme SacI, fragmenting enzyme NlaIII, and the type-IIS tagging enzyme MmeI to create 21 bp tags that are concatenated and sequenced to generate a representative genomic profile of the sample. Previous protocols for digital enumeration of DK genomic tags have provided a quantitative measure for copy number alterations of human loci, and have been generally used for identification of cancer gene deletion or amplification .
Second, for identification of pathogen genomes in human tissues, we compiled a database comprising all bacterial and viral genomes from existing databases and computationally generated a library that contains microbial virtual tags. The experimental tags that matched the human genome or that had no homology to known microbes are removed such that only exact 21 bp sequences corresponding to the microbial virtual tag library are revealed and used for further analysis.
Finally, BLAST  is used to verify tag matches to microbial genomes and to permit extraction of nearby sequences for primer design and PCR based confirmation.
Probability of detecting microbial DNA using DK-MICROBE
Probability of detecting microbial virtual tags using DK-MICROBE
Average microbial tag number per cell
Probability of detecting at least one virtual tag given the number of DK tags sequenced (%)^
Testing the presence of EBV tags in lymphoblastoid lines
Identification of murine retrovirus in xenograft lines
To further investigate the ability of DK-MICROBE to detect naturally occurring exogenous sequences, we examined tumor xenograft samples for the presence of murine retroviruses. It is recognized that xenotropic and ecotropic murine leukemia viruses (MuLVs) are endemic in nude mice and can productively infect transplanted tumor cells . The MuLV genome is approximately 8 kb, giving rise to only 4 virtual tags per genome. The fewer predicted virtual tags in this genome would make detection of these viruses more difficult at lower number of experimental tags than those with larger genomes such as EBV.
Implementation of DK-MICROBE for screening of human cancer samples
Tumor samples analyzed by DK-MICROBE*
# Tags Sequenced
Total Pathogen Tags
Pediatric Glioblastoma Multiforme
Colon Metastasis (Liver)
From all tumor samples, a total of 92 tags were identified as candidate microbial sequences (Additional file 4). Among the 92 tags, eight represented murine type C retroviruses (7 MTCRs and one Rauscher MuLV) in xenograft tumors, all of which we validated by PCR against the MTCR viral genome. Among the remaining tags, only a few have reported associations with human infection, including Klebsiella pneumonia  and Burkholderia cenocepacia . Several tags represent organisms associated with host environments other than the human, such as several plant symbiants and extremophilic species. Selected tags for the most relevant organisms were chosen for validation using PCR-based methods. However, these candidate microbe genomes were not detected in the samples tested, suggesting that infrequent DK tags matching exogenous genomic sequences may represent rare contaminating DNA sequences which are difficult to amplify, or may result from sequencing errors or variation in human genomic sequences that are identical to microbial sequences.
Microbial pathogens are the etiologic agents of many human diseases and are likely to be under-appreciated in other human illnesses, including cancer. The identification of previously unrecognized pathogens may advance the development of new diagnostic methods and may provide preventative and therapeutic strategies, such as targeted therapy or vaccines to limit tumor-initiating infections.
All current technologies for pathogen identification in human tissues suffer from some common limitations. Varying mechanisms of infection may confound the identification of foreign sequences, as the agent may be present at a very low load in the host cell or function by a transient (hit-and-run) mechanism. In such cases, successful detection of pathogenic sequences is highly reliant on infection stage and cellular composition of the sample. Additionally, these approaches may fail to identify pathogens whose genome composition is unknown. Such methods may also result in false positives, in part because of the similarity of some microbial sequences with the sequence of the host cell. Nevertheless, the overall results of DK-MICROBE indicate a remarkable degree of specificity, even if one assumes that all sequences other than HHV-6 and murine retroviral tags were the result of non-microbial sequences (false-positive rate < 0.001% (81/8,850,672)). As indicated above, the false positives identified by DK-MICROBE may be a result of homology to polymorphic human sequences, sequencing errors, or sequences not present in the current human genome databases.
Comparison of available technologies for identification of microbial sequences in human tissues
(A) Genomic DNA-based platform vs. RNA-based platform
Principle for Analysis
Extraction of sequences from expression library data sets
Detection of actively expressed microbial genes regardless of DNA copy number
No detection of non-transcribed microbial genes
Tags can be generated as long as genes are transcripted
Can miss detection if low microbial load
Tag numbers are not limited by the genome size
Subject to sequencing errors or tag site polymorphisms matching microbial sequences
Viral Detection DNA Microarray 
Extraction of sequences from genomic library data sets
Detection of microbial DNA regardless of expression status of the genes
No detection of non-reverse transcribed RNA
Potential to detect latent microbial genomic fragments which have been integrated into human genome
Can miss detection if low microbial load
Computational Subtraction 
Utilization of clinical samples not suitable for RNA extraction including paraffin-embedded fixed tissue
Subject to sequencing errors or tag site polymorphisms matching microbial sequences
(B) Tag-based vs. array hybridization
Principle for Analysis
Cross hybridization to homologous sequences of microorganisms on microarray
Currently lower cost
Array designed to target limited number of candidate microbes
Method is highly versatile and generally high throughput
Risk of unspecific binding
Fractional representations through extraction of sequences from specific locations in microbial genomes
Sequence based non biased result
Limited by sequencing costs
Greater chance to identify rare microbial associations
Subject to sequencing errors, tag site polymorphisms
No physical generation of organism-specific array
Results can be utilized and referenced for additional human genome analyses
In this study, we have developed an unbiased approach called DK-MICROBE, for discovery of viral and bacterial associations with human cancer. We validated the sensitivity and specificity of DK-MICROBE in EBV-infected cells, xenografts, and primary cancer tissues. In the normal lymphoblastoid cell line, DK-MICROBE detected all virtual tags of EBV which had been used to transform the cells. In two xenografts, we detected DNA of MuLV, which has a small genome containing only four virtual tags, but did not detect this organism in any of the primary tumors analyzed.
Use of DK-MICROBE identified the presence of HHV-6 DNA in a colorectal cancer metastasis, and subsequent analyses showed that that viral DNA from HHV-6 is present in a subset of colorectal tumors and normal tissues from such individuals. These results demonstrate the ability to discover previously uncharacterized exogenous DNA in primary cancers and tumor metastases using this approach. HHV-6 has been reported to infect a variety of human tissues, and its prevalence in the adult human population is > 85% . Although our data do not directly implicate HHV-6 in colorectal tumorigenesis (since the infection does not appear to be tumor specific), further studies will be needed to determine whether there may be an altered prevalence of colorectal cancer among HHV-6-infected individuals.
Although the studies performed here with DK-MICROBE did not clearly implicate a microbial pathogen in the tumors analyzed, it is clear that this approach has the power to detect exogenous sequences with high sensitivity. Given the association of a variety of viral and bacterial pathogens in the development of human cancer (see  for a recent review), it is likely that additional microbial culprits remain to be identified in human tumors. This sensitivity of DK-MICROBE is likely to increase in the future as next-generation sequencing approaches can be used to generate tag libraries of substantially greater sizes. Analysis of larger DK libraries from additional tumor types and samples will increase the probability of detecting unrecognized infectious agents that may play a role in these malignancies. DK-MICROBE may also be useful for analysis of other human diseases and for analysis of microbial populations in various microenvironments. With the complete sequencing of the human genome and further sequence knowledge of additional microbial genomes, the utility, sensitivity, and specificity of DK-MICROBE to detect exogenous sequences will continue to improve.
This work was supported by The Pediatric Brain Tumor Foundation Institute at Duke; a Damon Runyon Foundation Scholar Award; a Southeastern Brain Tumor Foundation Research Grant; an Alex's Lemonade Stand Foundation Innovation Award; a V Foundation Cancer Research Grant; NIH Grants R01CA118822, R01CA121113, NS20023-21, NS052507 and R37CA11898-34; Brain Tumor Specialized Programs of Research Excellence 5P20CA096890-02; Duke Comprehensive Cancer Center Support Grant 2P30CA14236; and grants from the Accelerate Brain Tumor Cure Foundation and the National Cancer Center, NINDS Grant 5P50 NS20023-25, NIH SPORE Grant 5P50 CA108786-4, NIH Merit Award R37 CA 011898-38, NCI Division of Cancer Prevention contract HHSN261200433002C, and The Pew Charitable Trusts.
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