# VIRUS DISCOVERY BY METAGENOMICS: THE (IM)POSSIBILITIES

EDITED BY: Bas E. Dutilh, Alejandro Reyes, Richard J. Hall and Katrine L. Whiteson PUBLISHED IN: Frontiers in Microbiology

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ISSN 1664-8714 ISBN 978-2-88945-308-5 DOI 10.3389/978-2-88945-308-5

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## **VIRUS DISCOVERY BY METAGENOMICS: THE (IM)POSSIBILITIES**

### Topic Editors:

**Bas E. Dutilh,** Utrecht University and Radboud University Medical Centre, Netherlands **Alejandro Reyes,** Max Planck Tandem Group in Computational Biology, Universidad de los Andes, Colombia; Washington University in Saint Louis, United States **Richard J. Hall,** Ministry for Primary Industries, New Zealand **Katrine L. Whiteson,** University of California, Irvine, United States

Acanthamoeba polyphaga Mimivirus is a member of the Mimiviridae that packages its huge genome via an aperture in the center of an icosahedral face. The 1,181,549 base pair linear dsDNA genome encodes 979 predicted open reading frames and 39 RNAs. It occurs in freshwater, soil, and host-associated habitats, where it infects its common host, the protozoa Acanthamoeba polyphaga using a lytic lifestyle.

Image: "Life in our phage world" by Forest Rohwer, Merry Youle, Heather Maughan, and Nao Hisakawa (2015phage.org/art.php). Artist: Ben Darby (www.darbyarts.com).

Since the late 1800s, the discovery of new viruses was a gradual process. Viruses were described one by one using a suite of techniques such as (electron) microscopy and viral culture. Investigators were usually interested in a disease state within an organism, and expeditions in viral ecology were rare. The advent of metagenomics using high-throughput sequencing has revolutionized not only the rate of virus discovery, but also the nature of the discoveries. For example, the viral ecology and etiology of many human diseases are being characterized, non-pathogenic viral commensals are ubiquitous, and the description of environmental viromes is making progress. This Frontiers in Virology Research Topic showcases how metagenomic and bioinformatic approaches have been combined to discover, classify and characterize novel viruses.

**Citation:** Dutilh, B. E., Reyes, A., Hall, R. J., Whiteson, K. L., eds. (2017). Virus Discovery by Metagenomics: The (Im)possibilities. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-308-5

# Table of Contents

### **The rise of viral metagenomics**

### **The roles of viruses in human and animal hosts**

### **Illuminating the dark matter of the global virosphere**

*84 Metagenomics of plant and fungal viruses reveals an abundance of persistent lifestyles*

Marilyn J. Roossinck

*87 Tales from the crypt and coral reef: the successes and challenges of identifying new herpesviruses using metagenomics*

Charlotte J. Houldcroft and Judith Breuer

Simon Roux, François Enault, Viviane Ravet, Olivier Pereira and Matthew B. Sullivan

Ramy K. Aziz, Bhakti Dwivedi, Sajia Akhter, Mya Breitbart and Robert A. Edwards

### **Bioinformatic advances driving the new discoveries**

*157 Finding and identifying the viral needle in the metagenomic haystack: trends and challenges*

Hayssam Soueidan, Louise-Amélie Schmitt, Thierry Candresse and Macha Nikolski

Cheryl-Emiliane T. Chow, Danielle M. Winget, Richard A. White III, Steven J. Hallam and Curtis A. Suttle

# Editorial: Virus Discovery by Metagenomics: The (Im)possibilities

#### Bas E. Dutilh1, 2 \*, Alejandro Reyes 3, 4, 5, Richard J. Hall 6 and Katrine L. Whiteson7

1 Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, Netherlands, 2 Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, Netherlands, 3 Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia, 4 Max Planck Tandem Group in Computational Biology, Universidad de los Andes, Bogotá, Colombia, 5 Center for Genome Sciences and Systems Biology, Washington University in Saint Louis, St Louis, MO, United States, 6 Animal Health Laboratory, Ministry for Primary Industries, Upper Hutt, New Zealand, 7 Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States

#### Keywords: metagenomics, virus discovery, virome, bacteriophages, phages, metagenome, bioinformatics, biological dark matter

#### **Editorial on the Research Topic**

#### **Virus Discovery by Metagenomics: The (Im)possibilities**

This Frontiers in Virology Research Topic showcases how metagenomic and bioinformatic approaches have been combined to discover, classify and characterize novel viruses. Since the late 1800s (Lecoq, 2001), the discovery of new viruses was a gradual process. Viruses were described one by one using a suite of techniques such as (electron) microscopy and viral culture. Investigators were usually interested in a disease state within an organism, and expeditions in viral ecology were rare. The advent of metagenomics using high-throughput sequencing has revolutionized not only the rate of virus discovery, but also the nature of the discoveries. For example, the viral ecology and etiology of many human diseases are being characterized, non-pathogenic viral commensals are ubiquitous, and the description of environmental viromes is making progress.

This accelerated rate of virus discovery comes both with fantastic possibilities and with significant risks. Metagenomics has already unveiled vast microbial biodiversity in a range of environments, and is increasingly being applied in clinics for difficult-to-diagnose cases. Hall et al. have contributed a thoughtful review on the challenges in using viral metagenomics for diagnostics, including handling of incidental findings, implications for agricultural and horticultural trade, privacy concerns relating to the host's genome, data sharing, cost, quality assurance, and etiology. Presently, the genomic era defines the viral universe by characterizing genotypes, but these genotypes are rarely associated with a phenotype and/or a physical entity. Moreover, the number and diversity of viral sequences in reference databases are dwarfed by the sequences from their cellular hosts. As a result, state of the art taxonomic classification of viruses recognizes only several thousand viral species, a large fraction of which infect humans. This stands in sharp contrast with the diversity of the cellular organisms on which all viruses depend for their replication.

Although fifteen years have passed since the first viral metagenome was sequenced from an ocean sample (Breitbart et al., 2002), the experimental and bioinformatic methods used for viral metagenomics have not reached a consensus. First, this reflects the diversity in applications ranging from virus discovery, to diagnostics, to ecological surveys. Second, this reflects the diversity in the microbial world itself that includes giant viruses (Halary et al., 2016), tiny bacteria (Brown et al., 2015), and everything in between. Thus, even the most basic experimental steps such as filtering viruses from an environmental sample need to be optimized for different applications. Viruses may employ diverse genomic molecular compositions, as illustrated by an RNA sequencing study that uncovered several single stranded and double-stranded RNA viruses in mosquitoes

#### Edited by:

Akihide Ryo, Yokohama City University, Japan

#### Reviewed by:

Masatsugu Obuchi, Toyama Institute of Health, Japan

> \*Correspondence: Bas E. Dutilh<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Virology, a section of the journal Frontiers in Microbiology

Received: 04 August 2017 Accepted: 23 August 2017 Published: 08 September 2017

#### Citation:

Dutilh BE, Reyes A, Hall RJ and Whiteson KL (2017) Editorial: Virus Discovery by Metagenomics: The (Im)possibilities. Front. Microbiol. 8:1710. doi: 10.3389/fmicb.2017.01710 (Chandler et al.). Roux and colleagues studied the uncultivated "Far-T4 phages" that are commonly found in aquatic environments, identifying five clades with largely collinear genome organizations (Roux et al.). Our Research Topic highlights the diversity of the virosphere in reviews on plant viruses where metagenomics has revealed an unexpected diversity in viruses with persistent lifestyles (Roossinck), and on coral reefs where herpes viruses were revealed in various host species (Houldcroft and Breuer).

A third reason for the diversity in viral metagenomics methods is the ongoing development of new tools and approaches, that are both cause and effect of our improved understanding of the virosphere. Bioinformatically, identifying viral sequences in a shotgun metagenomic dataset can be like finding a needle in a haystack (Soueidan et al.). Detection methods based on reference sequences can sensitively identify known viruses in short-read datasets (Pirovano et al.), but may limit the search to identify only known species. A promising possibility is to use short seeds to identify and progressively assemble viral sequences from the dataset, for example allowing the reconstruction of 45 partial or complete Alpavirinae genomes (Alves et al.). Alternatively, abundance and nucleotide usage signals can be used to identify de novo assembled metagenomic contigs belonging to the same genome, although the specificity of these binning signals varies (Smits et al.). Viral fosmids are a complementary approach allowing the recovery of long contiguous sequences, albeit at the cost of an inherently lower throughput. Chow et al. combined fosmid sequencing with shotgun metagenomics and database searches to chart the viral diversity in a Canadian fjord, elucidating genomic and ecological contexts, and identifying potential host interactions.

It has been known for decades that viruses infect economically important crops and animals including humans. However, a different view of viruses has recently emerged, the virome, a name for the entire community of viruses found in a given biome. For example, the human virome consists of the viruses that normally live on and within a human being, and includes viruses that infect the human itself, but also viruses ingested via food and the viruses infecting human-associated bacteria and archaea. Indeed, viruses are everywhere, and understanding the role of the virome within a complex ecosystem is a challenge at a whole new level. Aziz et al. developed various metrics to represent the presence of sequences in metagenomes and environments, and created a web tool showing the presence of a set of reference genomes in available metagenomes.

In our Research Topic, different approaches were taken. The bacteriophage adherence to mucus (BAM) model, which proposed that bacteriophages could assist the immune system of animal hosts by creating an external layer of defense in mucosal surfaces (Barr et al., 2013), has been extended to wild and farmed eels (Carda-Dieguez et al.). While the BAM model suggests that mainly lytic phages should benefit from this behavior, very similar Ig-like motifs to those originally implicated in mucus attachment were identified in temperate Pseudomonas aeruginosa phages (Tariq et al.). Those findings led to a proposed model incorporating BAM into the lifecycle of P. aeruginosa in cystic fibrosis patients.

Some viruses in host-associated viromes may chronically linger without causing any symptoms or phenotype, until their emergence is triggered, for example following debilitation of the host immune system in the case of eukaryotic viruses, or other environmental stresses in the case of temperate bacteriophages progressing into the lytic cycle. For example, Santiago-Rodriguez et al. identified a phage in Methicillin Resistant Staphylococcus aureus (MRSA) whose expression was inhibited in an ex vivo human blood model, suggesting preference for the lysogenic state in blood. The ecological question of virome stability is also relevant outside the context of a host, being linked to organismal diversity and nutrient release (Suttle, 2007). In our Research Topic, marker gene amplification studies targeting Picornavirales (Gustavsen et al.) and Gokushoviruses (Labonté et al.) in Canadian coastal waters show high viral diversity, both spatiotemporally and across a depth gradient. This variability suggests an important role for the viruses in structuring the bacterial and eukaryotic plankton community, as well as in nutrient cycling and energy transfer.

Santiago-Rodriguez et al. investigated whether the virome could be used as a sensitive marker of alterations in the health status of a host. While the urinary tract was long considered a sterile environment except during rare urinary tract infections (UTIs), it was recently shown that in fact, it contains an associated microbiome even in healthy individuals. Like many human-associated viromes, the urinary tract virome sequences were found to be dominated by bacteriophages. Only 27% of virome contigs were homologous to a known virus (similar to what other studies of human viromes find), and most of the hits matched bacteriophages. No significant changes were detected between healthy individuals and UTI patients (Santiago-Rodriguez et al.). Interestingly, human papillomavirus (HPV) was detected in 95% of the subjects, regardless of disease status. Traditionally, HPV was associated with diseases including cancer, but many HPV genotypes are now widely detected without any apparent association to disease. A similar case is a novel gamma-papillomavirus that was discovered in the virome of a patient with a respiratory tract infection (Canuti et al.). This HPV was present at equivalent titers during the respiratory infection and after the recovery, suggesting it was not the cause of the disease.

Linking unknown viral metagenomic sequencing reads to a function in a complex environment is often impossible, so in vitro systems to study novel phage bacteria interaction could serve as an ideal intermediate. For example, Gambelli et al. characterized the virome in a bioreactor containing a Methylomirabilis oxyfera enrichment culture, hoping to identify a phage that infected this important nitrogen cycling bacterium. While the shape and size of the virions could be modeled in high detail by advanced electron microscopy and three-dimensional imaging, and highthroughput metagenomic sequencing identified several very long bacteriophage contigs, it still proved challenging to identify which of the metagenomic sequences represented the phages the M. oxyfera phages seen in the images (Gambelli et al.). This impressive effort thus highlights some of the challenges we face on the road ahead toward a full understanding of viruses and their interactions in the natural environment.

## CONCLUSIONS AND OUTLOOK

Studying viral sequences means working at the edge of human knowledge. Even microbial genomics experts working on uncultivated microbes use the term "dark matter" when describing the viral sequences they find in metagenomes. While metagenomics expands our ability to detect viruses, a combination of small viral sequence databases and great diversity still means that many viral reads have no homology to known viruses (Mokili et al., 2012). Whether they are hostassociated or free-living, we now know that most viruses (like microbes in general) are not pathogenic to humans, plants, or animals. Recent technological advances including decreased DNA sequencing costs and the development of novel methods in metagenome analysis are making the study of viral communities feasible to many laboratories around the world. The Research Topic authors were motivated to identify novel viral agents of disease, illuminate the vast "dark matter" that is viral diversity, discover functional genes carried by bacteriophages, uncover how phages structure microbial communities, and perhaps

### REFERENCES

support renewed interest in phage therapy to target antibiotics resistant bacterial infections. The current Research Topic is an excellent compendium of manuscripts that, far from being comprehensive, we hope will form a foundation and inspiration for many other studies to come in the field of viral discovery, and motivate a new generation of microbial ecologists to include the viruses in their research.

### AUTHOR CONTRIBUTIONS

BD, AR, RH, and KW edited the Research Topic and wrote the Editorial.

### ACKNOWLEDGMENTS

We wish to thank all authors and reviewers of the manuscripts in this Research Topic "Virus discovery by metagenomics: the (im)possibilities" for their valuable contributions. BD was supported by the Netherlands Organization for Scientific Research NWO Vidi grant 864.14.004.

Mokili, J. L., Rohwer, F., and Dutilh, B. E. (2012). Metagenomics and future perspectives in virus discovery. Curr. Opin. Virol. 2, 63–77. doi: 10.1016/j.coviro.2011.12.004

Suttle, C. A. (2007). Marine viruses–major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812. doi: 10.1038/nrmicro1750

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Dutilh, Reyes, Hall and Whiteson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Beyond research: a primer for considerations on using viral metagenomics in the field and clinic

*Richard J. Hall1\*, Jenny L. Draper2, Fiona G. G. Nielsen3 and Bas E. Dutilh4,5,6\**

*1 Institute of Environmental Science and Research, National Centre for Biosecurity and Infectious Disease, Upper Hutt, New Zealand, 2 Ministry for Primary Industries Animal Health Laboratory, National Centre for Biosecurity and Infectious Disease, Upper Hutt, New Zealand, 3 DNAdigest, Future Business Centre, Cambridge, UK, 4 Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, Netherlands, 5 Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands, 6 Department of Marine Biology, Institute of Biology, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil*

*Edited by: Katrine L. Whiteson, University of California, Irvine, USA*

#### *Reviewed by:*

*Richard K. Plemper, Georgia State University, USA David T. Pride, University of California, San Diego, USA*

#### *\*Correspondence:*

*Richard J. Hall, Institute of Environmental Science and Research, National Centre for Biosecurity and Infectious Disease, 66 Ward Street, Upper Hutt 5018, New Zealand<EMAIL_ADDRESS>Bas E. Dutilh, Theoretical Biology and Bioinformatics, Utrecht University, Padualaan 8, 3584 CH, Utrecht, Netherlands<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Virology, a section of the journal Frontiers in Microbiology*

> *Received: 10 December 2014 Accepted: 06 March 2015 Published: 25 March 2015*

#### *Citation:*

*Hall RJ, Draper JL, Nielsen FGG and Dutilh BE (2015) Beyond research: a primer for considerations on using viral metagenomics in the field and clinic. Front. Microbiol. 6:224. doi: 10.3389/fmicb.2015.00224* Powered by recent advances in next-generation sequencing technologies, metagenomics has already unveiled vast microbial biodiversity in a range of environments, and is increasingly being applied in clinics for difficult-to-diagnose cases. It can be tempting to suggest that metagenomics could be used as a "universal test" for all pathogens without the need to conduct lengthy serial testing using specific assays. While this is an exciting prospect, there are issues that need to be addressed before metagenomic methods can be applied with rigor as a diagnostic tool, including the potential for incidental findings, unforeseen consequences for trade and regulatory authorities, privacy and cultural issues, data sharing, and appropriate reporting of results to end-users. These issues will require consideration and discussion across a range of disciplines, with inclusion of scientists, ethicists, clinicians, diagnosticians, health practitioners, and ultimately the public. Here, we provide a primer for consideration on some of these issues.

Keywords: viral metagenomics, ethics, medical, metagenomics, data interpretation, incidental findings, diagnostic tools, biomarkers

### Introduction

Efforts to discover and describe new viral species have lagged behind other organisms such as bacteria, fungi, or eukaryotes (Rosario and Breitbart, 2011; Delwart, 2013; Hurwitz and Sullivan, 2013). In part, this is due to the lack of a universal conserved genetic element shared between viral genomes which could be exploited for the purposes of viral genome discovery. For other microbes, there are conserved elements which can be targeted to detect new taxa, such as the 16S ribosomal RNA (rRNA) gene for bacteria, or the internal transcribed spacer (ITS) region for fungi. Next-generation sequencing in shotgun metagenomics has greatly increased the capacity for, and discovery of, new viruses (Mokili et al., 2012). This topic has been the subject of intense scientific effort and review since the first application of this technology in 2002 to describe uncultured viruses in the marine environment (Breitbart et al., 2002). Given the common application of metagenomics for virus discovery in basic and applied research, it is not surprising that there is a growing interest for use in a diagnostic capacity. Potential diagnostic applications of viral metagenomics span many areas, from horticulture (Kehoe et al., 2014) to veterinary medicine (Blomstrom, 2011; Belak et al., 2013) through to human health (Miller et al., 2013). Similarly, metagenomics can be used as a tool in forensics (Karlsson et al., 2013) and for monitoring environmental samples such as water quality (Ng et al., 2012).

### A Universal Test: Incidental Findings

In the example of human health, diagnostic tests traditionally proceed as follows. First, a clinician examines a patient and makes a provisional diagnosis, which may then require further laboratory testing. If an infectious disease is suspected, the clinician is likely to request that pathogen A, B, or C be tested for, and a diagnostic laboratory will use culture, antigen, or antibody detection (immunoassay), or molecular methods. This process is similar for the diagnosis of infectious disease in animals or plants. Syndromic diseases, such as influenzalike respiratory illness in humans, vesicular disease in livestock, or "virus-like symptoms" in plants may require a more complex diagnostic workup, which could take place as a multiplex PCR or parallel/serial specific testing. In general terms, most PCR, culture, or immunoassay-based diagnostic methods only provide evidence for the presence or absence of specific pathogens. This is often all that is required to enable appropriate therapy. Additional techniques may be needed if other attributes of a pathogen are to be determined, i.e., antiviral or antimicrobial resistance, or presence of virulence genes/toxins. In contrast, metagenomics has the potential to detect all known organisms within a sample in a single experiment, and is particularly useful for viruses given the lack of a universally conserved genetic element. Deep metagenomic sequencing has the potential to not only identify viruses with a high level of taxonomic resolution, but may also reveal other attributes that are clinically relevant, such as resistance to antiviral medications (Quinones-Mateu et al., 2014). Because of these advantages, metagenomics is being increasingly applied in diagnostics, and is already being offered as a commercial diagnostic service1*,*2 .

Thus far, the potential ethical implications of metagenomics to detect all organisms in a sample have received less attention, with a notable absence of discussion in relation to the detection and diagnosis of pathogens. For example, the technique has an inherent capability for incidental detection of pathogens that may not be relevant to the condition for which the patient is seeking treatment. It is therefore prudent to consider the possible impacts of an incidental diagnosis before proceeding with such a test. Consider the hypothetical example of a stool sample sent to a medical laboratory for norovirus testing. If the patient is HIVpositive and metagenomics is applied, then it is likely that HIV sequences will be detected in the metagenomic data (especially relevant for the RNA metagenome, also known as the metatranscriptome), given that up to 60% of HIV patients have detectable levels of HIV in their stool (van der Hoek et al., 1995). Both patient and physician need to be aware of this potential. In cases where such incidental findings are unwanted, a technical solution is possible: using bioinformatics, a panel of high-consequence

1http://www.pathogenica.com/

2http://www.aperiomics.com/

viruses (or pathogens of concern) could be rapidly filtered from the data before reporting took place.

The potential for incidental findings may seem alarming, but is not unprecedented. Technologies such as magnetic resonance imaging (MRI) or computer-tomography (CT) scanning can detect cancer as an incidental finding, but patients will usually be made aware of this possibility before consenting to a test. The physician will have a clear understanding of how to manage the finding of malignancy, should it be observed. Moreover, we observe a parallel with individualized complete human genome or exome sequencing, where an average human being carries approximately 250–300 loss-of-function variants in annotated genes, and 50–100 variants previously implicated in inherited disorders (Genomes Project et al., 2010). We contend that it is the responsibility of any researcher or diagnostician who uses metagenomics in a clinical setting to ensure that both the physician and patient are aware of the potential for detection of any pathogen, even those unrelated to their disease, and that informed consent information is clear about the processes in place for supporting the patient in case of incidental findings. There are, however, to our knowledge, not yet any established guidelines for how such informed consent should be handled in the case of clinical diagnosis using metagenomic assays, which makes the requirement of informed consent very difficult to implement in practice for individual testing labs.

For clinical exome or whole-genome sequencing of patients, recommendations on incidental findings have been developed by the American College of Medical Genetics and Genomics (ACMG), including: a "minimum list" of serious disease variants which are recommended to be reported from the genetic lab to the ordering clinician; a recommendation on the type of (likely) pathogenic incidental findings to report; guidelines for pre- and post-test counseling of the patient; and recommendations for a patient to opt-out of receiving incidental findings (Green et al., 2013). Guidelines for consent, processing and reporting of incidental findings for clinical metagenomic sequencing addressing the above issues are urgently required to ensure that adequate ethical considerations are met when handling and analyzing human metagenomic data.

### The Host Genome: Culture, Ethnicity, and the Law

A metagenomic dataset is likely to contain significant amounts of host genome sequence, depending on the sample type and sequencing protocol. Routine clinical samples in human health such as feces, swabs, tissue biopsy, sputum, and urine, are all likely to contain human DNA and RNA. In some countries, there are strict legal controls in place that govern the use of human tissue. For example, in New Zealand, consent of the patient for the use of their tissue for any future unspecified research purposes is an absolute statutory requirement (Ministry of Health, 2007), and the tissue may only be used for the express intention for which it was collected (New Zealand Government, 2008). In particular, the indigenous people of New Zealand, Maori, have a unique cultural perspective on the ownership ¯ of human genetic information, which is not held to be the property of the individual, and decisions about investigating DNA should be made collectively (Baird et al., 1995; Hudson, 2009; New Zealand Health Research Council, 2010). Thus, it is possible that the incidental sequencing of human DNA could lead to legal, ethical, and/or cultural obligations when conducting a metagenomic analysis. Of course, the controls around sequencing of human DNA will vary greatly between jurisdictions, but the implications of generating incidental information should be accounted for. Once again, technical solutions such as bioinformatic filtering of the data for human DNA may provide a solution, but there would need to be some confidence around the quality of this process for deleting human genomic sequences. Without the removal of human sequence, human metagenomic datasets are likely to be subject to legislation for health-related personal data, which implies requirements for subject anonymization and restricted data access (Mascalzoni et al., 2014). To ensure the highest level of sharing and deposition of metagenomic data in research data repositories, we urge repositories and ethical review committees to develop guidelines on which methods are considered adequate for the removal of potentially personally identifiable human sequences from metagenomic data sets, to allow for deposition and sharing of metagenomic data without restrictions. Comprehensive guidelines should take into consideration both the benefit of data usage to inform research and any potential harm for patients as a result of non-compliance to guidelines. Consequences for a breach of ethical approval, and procedures to recall shared data in the event of a breach should also be considered. The ethical considerations for developing governance and guidelines have been discussed in depth by the Nuffield Council on Bioethics in their report concerning the collection and usage of biomedical data for research and health care (Nuffield Council on Bioethics, 2014).

### Trade Implications

The detection of pathogens of agricultural or horticultural significance can have dire consequences for productivity and trade. Most nations have government veterinary or plant health laboratories that provide testing and surveillance for significant pathogens. These laboratories specialize in screening animals and goods for trade purposes, preventing the spread of pathogens, and playing a crucial role in demonstrating that a country or region is free of specific diseases. In the case of trade in animals and animal products, the World Organization for Animal Health (OIE) maintains a list of the veterinary diseases of greatest concern worldwide, tracks, and reports on their global occurrence, and sets diagnostic testing standards for these diseases to aid international trade. New outbreaks of OIE-listed diseases in countries previously considered free of the disease can have severe trade implications, resulting in either blockage of relevant trade permits, or a massive increase in the testing and documentation required to certify products for export. The cost of such outbreaks can greatly affect tradebased economies, and proving freedom from such a disease once it is detected (or even suspected) can be a major economic burden.

Due to the sensitivity and untargeted nature of metagenomics, its application to animal or plant samples presents a challenge for regulatory authorities (MacDiarmid et al., 2013). Methods for assigning metagenomic sequencing reads to species may not be perfect, and incomplete reference databases as well as evolutionary conservation between species can easily lead to an incorrect diagnosis. Additionally, genetic databases are still skewed towards heavily researched organisms – particularly pathogens – and this bias increases the chance of relatively benign viruses being classified as "similar to" viruses of significant agricultural or clinical concern. A hypothetical example of a fish metagenome can be used to illustrate this point. Such a metagenome could conceivably contain sequences from a relatively benign virus in the family *Orthomyxoviridae*, which has some genetic similarity to the trade-sensitive *Isavirus* that causes Infectious Salmon Anemia (ISA) in salmon. If such a sequence, found in a healthy fish, is reported as "similar to ISA virus," this finding could have significant implications for trade in salmon, given that even the suggestion that an OIE-listed virus may be present can be enough to impose significant trade restrictions on an exporting country.

Responsibility must be taken by researchers and diagnosticians performing metagenomic analyses to ensure the veracity of their results and to engage with regulatory authorities early on in the process once an initial discovery has been made, to avoid unintended economic harm from falsely declaring the presence of an organism with trade implications.

### Use of the Data

It is important that metagenomic datasets are freely and openly shared upon the publication of scientific results, for example by deposition in a database such as EBI Metagenomics (Hunter et al., 2014), MG-RAST (Meyer et al., 2008), or Genbank (Benson et al., 2014). This allows other researchers to investigate the data, to substantiate or refute claims made by the original authors, and enables alternative research projects to be developed for which the samples were not originally obtained. Sharing metagenomic datasets also allows significant scientific discoveries to be made (Dutilh et al., 2014), and restricting access to datasets can seriously delay scientific progress. For example, the Fourth Paradigm of data-driven scientific discovery describes how the advancement of a scientific field depends on how well researchers collaborate with one another (Hey et al., 2009).

However, there are several concerns when it comes to publishing diagnostic metagenomic datasets. For example, as we discuss here, misinterpretation can have serious consequences. Moreover, there is a possibility that pathogens could be missed by the original submitters, or even discovered at a later date.

Additionally, in human diagnostic settings sharing of metagenomic data becomes ethically complex due to issues of patient confidentiality and privacy infringements. For example, if human samples were obtained for a specific purpose like virus discovery, then ethical permissions may only be granted for that purpose alone. If a researcher were to investigate any other aspect of the metagenomic data – such as looking for a correlation between a human genetic mutation and infection status or outcome – this would be in breach of the original ethical approval and/or statutory obligations.

## Expectations of the End-User

Ultimately, a decision must be made on how to act upon the result of a diagnostic test. Such decisions are made every day by clinicians, veterinarians, ecologists, epidemiologists, scientists, farmers, and private citizens. Commercially available tests for pathogen detection often condense a reported result into a binary "presence or absence" call, with much of the technical detail being deliberately and carefully hidden, to make interpretation easy. Metagenomic analyses currently provide end-users with a "shotgun" picture of the microbiome that includes a list of the organisms that are theoretically present based on sequence similarity. Interpretation relies on an expert examining the data and making assessments based on their experience, particularly in regard to the reliability of the methodology, nuances between viral or bacterial strains, genetic similarities between viruses, bacteria, and eukaryotes, and the potential for contamination of genome databases (Gonzalez et al., 2014; Merchant et al., 2014) and nucleic acid extraction kits (Salter et al., 2014). Therefore, the reports currently generated for metagenomic datasets are not yet conducive to widespread use, as witnessed by the recent mass public reporting of the alleged detection of anthrax and the bubonic plague on the New York City subway system, based on a metagenomic analysis which contained potential but non-definitive hits to the these pathogens (Afshinnekoo et al., 2015; Mason, 2015; Yong, 2015).

At least in the US, no tests for human genetic testing are allowed to enter the market as medical devices without strict analytical and clinical validation to ensure consistent and robust results. One recent example is the case of the direct-to-consumer (DTC) human genotyping service provided by the company 23andMe. In November 2013, the 23andMe Personal Genome Service product was banned from marketing and providing "medical reports" of "health risks" and "drug response" when the company failed to deliver adequate evidence to the FDA for validation of specificity and sensitivity (Woods, 2013). Similarly, we see a big challenge for the metagenomics community to develop robust analysis methods before metagenomics can be approved for widespread clinical use. We note that, as of February 2015, the FDA has eased access to DTC DNA screening for several inherited diseases.

Another recent case illustrates the dangers of misinterpretation of metagenomic analysis (based on unpublished data). In this case, an automated metagenomic pipeline, MG-RAST (Meyer et al., 2008) was used to analyze a metagenomic dataset from environmental sample source. Prior to the data analysis, a worker involved in sampling was suffering from an undiagnosed illness. During the preliminary analysis a sequence hit to a Risk Group 3 pathogen was observed in the results from MG-RAST, and the group involved in the sampling suspected that this could be the cause of the worker's illness. When the affected worker requested testing and prophylactic treatment for this notifiable zoonotic disease at a clinic, government organizations in human and animal health became involved. However, upon detailed review of the data, it was revealed that the "sequence hit" in MG-RAST actually matched a known archaeal contaminant in the draft genome assembly of the pathogen, rather than the pathogen itself. Although the contaminant sequences were clearly unrelated to the pathogen and had already been retracted from Genbank, they were still present in the automated annotation pipeline, which relied on an outdated database. We anticipate that such situations are likely to become increasingly common as metagenomics becomes mainstream.

It is our experience that when collaborators are presented with the taxonomic report from a metagenomic study for the first time, they may be overwhelmed. After some time spent studying the data, some even become skeptical. This is due to three factors: (i) the sheer enormity of microbial taxonomic diversity in any given sample, (ii) the confounding effect of gene conservation between taxonomic groups, and (iii) the potential for relative scarcity of reads representing a pathogen, even in samples with a significant viral load. Another example in our experience was revealed when a collaborator asked why a small number of cetacean sequence reads (derived from whales and dolphins) were present within the metagenomic data from an animal slaughterhouse that was processing cattle and sheep (Hall et al., 2013). This particular study was aimed at virus discovery, and was designed to be very sensitive, using sequence similarity search parameters that allowed identification of distantly related hits to enable the detection of novel viruses with low-level homology to known sequences. However, especially in eukaryotes, genetic sequences can be highly conserved, and in this case, the homologous matches occurred due to genetic conservation between the spuriously observed cetaceans, and ruminants such as the cattle and sheep processed in the slaughterhouse.

Explaining such cases to collaborating researchers takes time and careful communication, to allay their concerns about the inherent inaccuracies of the metagenomic method. Imagine, then, how difficult it could be to allay the concerns of a patient reading a report containing a non-significant hit to smallpox, or a farmer reading a report containing a spurious hit to foot-andmouth disease.

### Considerations for Clinical Use of Metagenomics

Before incorporating metagenomics into routine clinical diagnostics, viral databases need to be vastly expanded, so that sequences can be more accurately annotated (Dutilh, 2014). Thus, efforts to map the complete viromes of humans and economically relevant animal or crop species will provide a baseline for allowing metagenomics to be applied in the clinic. Moreover, a good reference database allows novel viruses to be readily detected. Novel viruses that are observed in humans for the first time, e.g., after genomic recombination of known viruses or by zoonotic transfer from risk species like bats, can be flagged as potentially dangerous (Temmam et al., 2014).

Moreover, general considerations need to be made, such as the time taken to generate results, how these results are reported, how performance attributes like sensitivity are assessed, and how quality assurance programs and criteria for accreditation should be developed. Indeed, recent concerns about the potential for false-positive detection of pathogens in metagenomics datasets (Naccache et al., 2014; Rosseel et al., 2014) underscore the need to develop proper quality control procedures before routine deployment of metagenomics in the clinic or diagnostic laboratory.

Large-scale deployment of diagnostic methods in clinical laboratories is facilitated by simplicity, repeatability, low costs, established quality assurance programs, and quick turn-around times for the production of results. The time and cost of processing a sample through a next-generation sequencer, albeit rapidly reducing, is still prohibitive when considering large scale diagnostic testing. Additionally, metagenomics is currently too complex for immediate release into the diagnostic laboratory. There is a lack of standardization in the laboratory methods applied, such as the choice of sequencing platform or upstream sample preparation that is used. The *ad hoc* and heterogeneous tools currently employed for the analysis of high-throughput metagenomic datasets will also need to be further streamlined and unified. Reliability values are

needed to account for the conservation of identifying sequences between pathogens and non-pathogens. In addition, depending on the methodology used, even a virus present in high titer might be represented by only a few reads in a metagenomic dataset. Thus, protocols need to be optimized, and in cases where coverage of a potential pathogen is low, other diagnostic methods, such as PCR, culture, immunoassays, or electron microscopy may be necessary to confirm the presence of the pathogen.

Nucleic acid amplification technologies (NAAT) such as realtime (quantitative) PCR already offer a rapid, cheap, sensitive and specific test for application in a broad range of settings (Gray and Coupland, 2014). Existing NAAT diagnostic tests already have a high degree of utility and meet diagnostic requirements for many areas – namely those requiring the detection of specific pathogens. Ultimately, the most valuable application of metagenomics in the clinic may be to replace serial testing/multiple single-plex assays, by offering a universal metagenomics-based test. Costs and turn-around times will still need to reduce significantly, and as mentioned above, quality assurance and method standardization are areas that will need major development. The Critical Assessment of Metagenome Interpretation3 (CAMI) is currently addressing these bioinformatic challenges in the form of a competition, by inviting

3http://www.cami-challenge.org/

tool developers to compete in the analysis of defined metagenomic datasets. CAMI evaluates the metagenome analysis tools and methods independently, comprehensively, and without bias. Thus, CAMI is paving the way for consistent and reliable metagenome interpretation tools to surface and receive international recognition.

### Etiology

Many end users will view metagenomics as a new technology, and in terms of application outside of a research setting, it certainly is. With the advent of new technologies come high expectations. For example, given the widely held apocryphal notion that many unsolved diseases are caused by viruses (Lipkin, 2014), will such diseases of unknown etiology now be resolved? Depending on the situation, this may or may not be the case, but it is certain that there are many other possible reasons for an unresolved etiology, such as an inadequate specimen or a diagnostic test that did not include the relevant pathogen, or a toxigenic or genetic cause of the disease.

One criticism of metagenomic virus discovery projects is that the mere detection of a micro-organism in a disease-state is not sufficient to establish etiology (Canuti et al., 2014). However, this holds equally true for any other detection protocol. There are certainly high profile instances of a "virus in search of a disease," and of a "disease in search of a virus". This has become particularly apparent for syndromic diseases such as encephalitis, where orthodox testing regimes have failed to identify a cause, and for cancers or autoimmune disease of unknown etiology. Evidence for the involvement of viruses in some cases of type 1 diabetes, inflammatory bowel disease, and asthma has recently been summarized (Foxman and Iwasaki, 2011). However, it is now generally recognized and accepted that viral metagenomics is primarily suitable as a discovery and detection method, and that any claims made in regard to etiology require extensive supporting information to fulfill Koch's postulates, or variations thereof where applicable (Mokili et al., 2012).

Another criticism is that virus discovery, including viral metagenomics, is a descriptive research field that lacks hypotheses or the pursuit of knowledge about higher level biological processes. While this is a fair criticism, it should not be used to hinder or halt efforts to discover new viruses. The processes of infection, pathogenesis, or disease ecology cannot be fully understood without a basic fundamental description of the viral ecosystem, e.g., of the human virome in the case of the human body. Similarly, the development of therapeutics, vaccines, and culture methods may be informed by the discovery of new viruses. Highly divergent viral genomes may provide information about critically conserved genes and thus reveal targets for antiviral therapies, or epitopes for vaccine development. Finally, the notoriously prevalent "unknowns" in viral metagenomes can only be resolved if

### References

Afshinnekoo, E., Meydan, C., Chowdhury, S., Jaroudi, D., Boyer, C., Bernstein, N., et al. (2015). Geospatial resolution of human and bacterial diversity with city-scale metagenomics. *Cell Syst.* (in press). doi: 10.1016/j.cels.2015.01.001

we face the grand challenge of mapping viral sequence space first (Dutilh, 2014).

### Conclusion

Virus discovery by metagenomics is still a fresh and developing field. Huge gains have already been made in the discovery of new viral species in a wide range of host species and samples (several examples can be found in the Frontiers in Virology Research Topic "Virus discovery by metagenomics: the (im)possibilities" of which this article is part). However, the application of viral metagenomics outside of the research setting remains relatively unexplored. Importantly, the issues surrounding the use of these methods to complement or replace existing clinical diagnostic tools need to be discussed in detail. The increase in sequencing efforts to characterize the viromes of various host species will lay a foundation for further analysis of viral metagenomes by providing a reliable reference database. To facilitate this, metagenomic datasets need to be made publicly available and mined (Dutilh, 2014), but at the same time this needs to be balanced against ethical, legal, and cultural factors and potentially include filtering steps to remove sequences matching the human reference, as a safeguard for the privacy of the individual. Incidental detection or spurious reporting of viruses, especially those of high consequence for health or trade, will require special consideration. Moreover, any application of viral metagenomics outside of a research project, for example for use in the clinic, will require good communication between patients, clinicians, and scientists, including informed consent about the handling and reporting of any incidental findings. In the case of agriculture, similar communication and agreement is required between the production sector, veterinarians, and regulators. We present a summary of the seven main points of concern and our proposed actions for resolving these in **Table 1**.

History shows that care is required when delivering a new and potentially disruptive technology. No doubt, larger debate and more deeply held concerns lie ahead in the area of sequencing eukaryotic genomics, especially for the human genome, but it is worth beginning the discussion on where viral metagenomics is heading, beyond research, on the way towards application of viral metagenomics in the field and clinic.

### Acknowledgments

We would like to thank Deborah Williamson and Don Bandaranayake from ESR, Christopher Weisener and Subbarao Chaganti from the University of Windsor, and Donna Gardiner from Nga Pae o te M ¯ aramatanga for their helpful comments on ¯ the manuscript.

Baird, D., Greening, L., Saville-Smith, K., Thompson, L., and Tuhipa, T. (1995). "Whose genes are they anyway?" in *Proceedings of the HRC Conference on Human Genetic Information*, Wellington.

Belak, S., Karlsson, O. E., Blomstrom, A. L., Berg, M., and Granberg, F. (2013). New viruses in veterinary medicine, detected by metagenomic approaches. *Vet. Microbiol.* 165, 95–101. doi: 10.1016/j.vetmic.2013. 01.022

a Potyvirus. *PLoS ONE* 9:e104580. doi: 10.1371/journal.pone. 0104580

10/theres-no-plague-on-the-nyc-subway-no-platypuses-either/ [accessed February 20, 2015].

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Hall, Draper, Nielsen and Dutilh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Metagenomic analysis of a sample from a patient with respiratory tract infection reveals the presence of a **γ**-papillomavirus

*Marta Canuti 1†, Martin Deijs1†, Seyed M. Jazaeri Farsani 1, Melle Holwerda1, Maarten F. Jebbink1, Michel de Vries 2 , Saskia van Vugt 3 , Curt Brugman3 , Theo Verheij 3 , Christine Lammens 4 , Herman Goossens 4 , Katherine Loens 4 , Margareta Ieven4 and Lia van der Hoek1\**

1 Laboratory of Experimental Virology, Department of Medical Microbiology, Center for Infection and Immunity Amsterdam, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands

2 CBS-KNAW Fungal Biodiversity Center, Utrecht, Netherlands

3 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands

4 Department of Medical Microbiology, Vaccine and Infectious Disease Institute, Universiteit Antwerpen–University Hospital Antwerp, Antwerp, Belgium

#### *Edited by:*

Bas E. Dutilh, Radboud University Medical Center, Netherlands

#### *Reviewed by:*

Chris Sullivan, University of Texas at Austin, USA Jan Zoll, Radboud University Medical Center, Netherlands

#### *\*Correspondence:*

Lia van der Hoek, Laboratory of Experimental Virology, Department of Medical Microbiology, Center for Infection and Immunity Amsterdam, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, Netherlands e-mail<EMAIL_ADDRESS>
†Marta Canuti and Martin Deijs have contributed equally to this work.

Previously unknown or unexpected pathogens may be responsible for that proportion of respiratory diseases in which a causative agent cannot be identified. The application of broad-spectrum, sequence independent virus discovery techniques may be useful to reduce this proportion and widen our knowledge about respiratory pathogens. Thanks to the availability of high-throughput sequencing (HTS) technology, it became today possible to detect viruses which are present at a very low load, but the clinical relevance of those viruses must be investigated. In this study we used VIDISCA-454, a restriction enzyme based virus discovery method that utilizes Roche 454 HTS system, on a nasal swab collected from a subject with respiratory complaints. A γ-papillomavirus was detected (complete genome: 7142 bp) and its role in disease was investigated. Respiratory samples collected both during the acute phase of the illness and 2 weeks after full recovery contained the virus. The patient presented antibodies directed against the virus but there was no difference between IgG levels in blood samples collected during the acute phase and 2 weeks after full recovery. We therefore concluded that the detected γ-papillomavirus is unlikely to be the causative agent of the respiratory complaints and its presence in the nose of the patient is not related to the disease. Although HTS based virus discovery techniques proved their great potential as a tool to clarify the etiology of some infectious diseases, the obtained information must be subjected to cautious interpretations. This study underlines the crucial importance of performing careful investigations on viruses identified when applying sensitive virus discovery techniques, since the mere identification of a virus and its presence in a clinical sample are not satisfactory proofs to establish a causative link with a disease.

**Keywords: virus discovery, papillomavirus, respiratory tract infection, VIDISCA-454, GRACE**

#### **INTRODUCTION**

Acute respiratory tract infections (ARTIs) can be caused by a wide variety of pathogens, among which viruses and bacteria are the main agents involved. Still, even with the sensitive diagnostic assays employed nowadays, a substantial amount of respiratory diseases cannot be attributed to any of the known commonly involved microorganisms (Tsolia et al., 2004; van de Pol et al., 2006; Regamey et al., 2008).

The existence of previously unknown or unexpected respiratory pathogens can be postulated as an explanation of this phenomenon. Furthermore, the pool of human respiratory viruses keeps growing because of the continuous introduction of "novel" pathogenic viruses from the animal reservoir (Neumann et al., 2009; Memish et al., 2014). These viruses need to be quickly identified and characterized, since they might represent a significant public health concern (Peiris et al., 2004).

In both cases the application of broad-spectrum virus discovery techniques can be useful to detect previously unrecognized or emerging pathogens, and the introduction in the market of high-throughput sequencing (HTS) methodologies considerably improved the efficacy of such methods (Mokili et al., 2012).

One of these methods is VIDISCA-454, a restriction enzyme based virus discovery technique which was developed in our laboratory and utilizes Roche-454 as HTS method (de Vries et al., 2011, 2012). This technique provides the broad-spectrum approach needed to identify novel or unexpected viruses (Oude Munnink et al., 2014). In this study, we used VIDISCA-454 to investigate a nasal swab – that tested negative to the principal respiratory pathogens – collected from a patient with respiratory complaints to determine whether an unknown virus could be the cause of the disease.

Among the obtained sequence reads an unexpected virus was found, a papillomavirus. Papillomaviruses carry a circular double stranded DNA genome, are non-enveloped and are known for their capacity to induce warts and benign lesions of mucous membranes (condylomas; Howley and Lowy, 2007). Some human papillomaviruses (HPVs) can cause cancers, of which cervical cancer is the most notorious (Bosch et al., 1995). HPVs are not known as causative agents of respiratory infections, although some studies report the presence of different HPVs types in the respiratory tract of infants with respiratory diseases but also in healthy adults (Kurose et al., 2004; Martinelli et al., 2012; Forslund et al., 2013; Mokili et al., 2013). HPVs are also involved in recurrent respiratory papillomatosis (RRP), a disease characterized by growth of tumors around the vocal cords and in the larynx (Mammas et al., 2014). The pathogenic role of HPVs seems to be genus/type specific: cervical cancer and RRP are induced by α-HPVs (particularly types 16, 18, in cervical cancer, type 11 and 6 in RRP), while γ-HPVs are known to be causative agents of warts or skin lesions. Furthermore, several HPVs, especially from the δ- and γgenus, can be detected on healthy normal skin (Astori et al., 1998; Antonsson et al., 2000; Li et al., 2012).

Since the high sensitivity of HTS based virus discovery methods makes it possible to identify viruses which are present in a clinical sample at a very low concentration, questions about the clinical relevance of those microorganisms might rise. The scope of this study was to determine whether a correlation could be demonstrated between the γ-HPV – identified with VIDISCA-454 – and the clinical respiratory manifestations of the patient.

### **MATERIALS AND METHODS**

#### **PATIENT INFORMATION**

The sample was collected from a 64-year-old heavy smoking male patient (defined from now on as the index patient) during a visit to his local general practitioner where he reported symptoms of a respiratory tract infection. The onset of symptoms was 2 days before visiting his general practitioner. Symptoms included cough, phlegm production, shortness of breath, wheeze, coryza, fever, chest pain, muscle aching, and headache. During this first visit respiratory specimens (nasopharyngeal flocked swabs: NPFS, Copan) from each nostril (one in universal transport medium: UTM – the other in skimmed milk) and serum were collected (Visit 1: V1). The patient was part of the GRACE study, a randomized antibiotics placebo-controlled double-blind trial (www.grace-lrti.org), and received antibiotics (amoxicillin). A chest X-ray was performed and the patient kept a diary to monitor the course of symptoms and their severity. Fourteen days after onset of symptoms the patient fully recovered. Four weeks after the first visit the patient underwent a check up visit and again specimens from each nostril and serum were collected (Visit 2: V2). The NPFS of the first and second visit were screened for the presence of known respiratory pathogens associated with ARTI: adenovirus, respiratory syncytial virus, human metapneumovirus, influenza A and B, parainfluenza viruses 1–4, human bocavirus, human coronaviruses (229E,OC43, and NL63), human rhinovirus, polyomaviruses WU and KI, *Mycoplasma pneumoniae*, *Chlamydophila pneumoniae*, *Bordetella pertussis*, *Legionella pneumophila*, *Streptococcus pneumoniae,* and

*Haemophilus* spp (Loens et al., 2012). All diagnostics remained negative.

#### **ETHICAL APPROVAL**

Ethics review committee of Cardiff and Southampton (UK) approved the study and written informed consent was provided by the participants (Loens et al., 2012).

#### **VIRUS DISCOVERY: VIDISCA-454**

VIDISCA-454 (virus discovery cDNA-AFLP, amplified fragmentlength polymorphism combined with Roche 454 high-throughput sequencing) was performed with 110 μl of sample resuspended in UTM as previously described (deVries et al.,2011). Obtained reads were compared to known sequences present in the non-redundant database using the BLAST tool (Altschul et al., 1990).

#### **FULL GENOME SEQUENCING**

The γ-papillomavirus fragments identified after performing VIDISCA-454 were used as template for primer design and PCRs were performed using the Expand Long Template PCR system (Roche). The complete genome was obtained by sequencing two large amplified fragments which were overlapping on both sides for at least 400 nucleotides. Fragments were cloned into a XL-TOPO-kit (invitrogen), and sequenced (BigDye® Terminator v1.1 Cycle Sequencing Kit, Applied Biosystems). Primer sequences used in this study are available on request.

#### **PROTEIN EXPRESSION**

The full late gene L1 (coding for the major capsid protein) was amplified with the Expand Long Template PCR system (Roche), cloned into pET100D expression vector (Invitrogen), and transformed into chemically competent Escherichia coli BL21-derived strain Rosetta 2 (Novagen). Overnight cultures of the transformed bacteria were inoculated into Luria broth medium, supplemented with 1% glucose, carbenicillin (10 μg/ml), and chloramphenicol (17.5 μg/ml). Cultures were grown to the exponential phase prior to induction with 0.5 mM isopropyl-β-D-thiogalactopyranoside (IPTG) for 5 h. Samples were collected every hour to monitor the L1-protein production. Attempts to purify the protein via Ni-NTA resin (Qiagen) failed, probably due to the formation of aggregates that shield the HIS-Tags; therefore the crude harvest was used to determine the reactivity of V1 and V2 serum on Western blot.

#### **SDS PAGE AND WESTERN BLOT**

Protein production on SDS-Page was visualized by coomassie staining and SDS-page gels were blotted onto PVDF (Millipore) membranes. The blots were incubated with 1:600 dilutions of either V1 or V2 serum, and with a 1:1000 dilution of a rabbit anti HIS-tag, followed by incubation with anti-human IRDye800CW (1:5000, Jackson Immunoresearch) and anti-rabbit DyLight649 (1:5000). Signals were measured via the Odyssey infrared imaging system (LI-COR). The intensity of the signals was normalized with the anti-HIS signal to correct for difference in protein concentrations.

#### **SCREENING BY REAL TIME PCR**

Real time PCRs were performed with the Platinum® qPCR-kit (Invitrogen) on an ABI PRISM 7000 sequence detection system of Applied biosystems according to the manufacturer's protocol, using the following primers and probe: RTForward-540–570: 5- -CATACCCTAACGAAGAGGTAGCAGAC-3- , RTReverse-675– 700: 5- -TGGCGGGCAACTGCCTTTATCTAG-3- , RTProbe-620– 650: 5- FAM- GGGCTGTATTCCGCCAACTGGTGAATATTGGG-ATG -TAMRA 3- .

Screening was performed in duplicate on 54 respiratory samples collected from patients with respiratory complaints involved in the GRACE study (Loens et al., 2012; 25 of which from the same city of the index patient) and only samples having detectable virus in both tests were considered positive.

#### **RESULTS**

A NPFS was collected from a 64 years old man with respiratory symptoms and the diagnostics for the most common respiratory viruses and bacteria remained negative. After performing virus discovery a total of 13,452 sequence reads were obtained and compared with all known sequences in GenBank (NCBI). Among these, 10 sequences showed significant identity to HPVs. By genome walking the entire sequence of the virus (named HPV isolate A2619) was determined: the viral genome is 7142 bp in size, circular and 99% identical to the recently described HPV-KC5 isolate (GenBank accession number: JX444073; Li et al., 2012).

#### **CORRELATION BETWEEN HPV-A2619 AND THE RESPIRATORY DISEASE**

The γ-HPVs are not known as respiratory pathogens and their role in any disease is still uncertain. Therefore, we considered it important to investigate the possible correlation between this virus and the respiratory symptoms of the patient: specific diagnostic tests were developed and applied.

Since the patient fully recovered after 14 days of illness, an increase of the specific antibody response to the responsible pathogen was expected, along with viral clearance. We examined the clearance of the virus with an HPV-A2619 strain specific real time PCR targeting the L1 gene. The virus was detected in the NPFS of both nostrils at V1, but also in the NPFS of both nostrils after the patient recovered (V2). The viral load in all samples was very low, between 2E3 and 2E4 copies/mL, and the Ct values were similar for all samples (V1-right nostril 38.93, V1-left nostril 40.60, V2-right nostril 42.90, and V2-left nostril 37.47).

Furthermore, western blot analysis was used to detect antibodies directed against the L1 capsid protein in serum samples collected at the same time as the 2 respiratory samples. A poor recognition of the protein was observed and no difference between V1 or V2 serum was noticed: the two samples had similar intensity ratios (10.78% for V1 serum; 10.76% for V2 serum; **Figure 1**).

#### **SCREENING**

Respiratory samples of 54 other patients from the GRACE study with respiratory tract infection were used for prevalence inference. Of these, 25 were from the same geographical area of the index patient and 39 from other European cities. Two patients (3.7%) were positive for HPV-A2619. In both patients also a low viral copy number was detected (between 2E3 and 2E4 copies/ml). Both patients came from the same city in the UK as the index case.

### **DISCUSSION**

#### **γ-HPVs AND RESPIRATORY INFECTIONS**

Not much is known about γ-papillomaviruses (γ-HPVs). Occasionally γ-HPVs have been related to skin warts or keratotic lesions of immunosuppressed organ transplant recipients (e.g., HPV-4; Köhler et al., 2011), but these viruses can also be detected on healthy skin (Astori et al., 1998; Antonsson et al., 2000; Li et al., 2012), indicating that they might not be pathogenic in healthy individuals or only when specific conditions are fulfilled. We identified a γ-HPV in the respiratory tract of a patient with symptoms of a respiratory tract infection but, since the virus was still detectable after full recovery and no rise in antibodies was observed, we concluded that the virus was not the causative agent of the respiratory disease of this patient. Careful inspection of the chest X-ray also did not reveal any cancer-related abnormalities within the lungs; furthermore also no skin warts or abnormalities on the skin were noted. It is therefore most likely that HPV-A2619, which we describe here, is a harmless and ubiquitous γ-HPV which can be present in nostrils.

HPV-A2619 resulted 99% identical to the previously reported HPV-KC5 which was isolated from normal skin of healthy individuals in a rural area in China (Li et al., 2012), suggesting that the virus is not geographically restricted to one location, as our screening results might have indicated. Furthermore, it might be argued that the virus was introduced in the respiratory tract after contact with contaminated skin during nasal swabbing, but the fact that the virus was persistently present in each nostril at two time points suggests that virus is genuinely present in the nose, as well as on normal skin.

In literature it is described that some types of HPV can cause human papillomatosis, lesions in the respiratory tract that need to be surgically removed, and in most cases it is a recurring problem and multiple surgical treatments are needed (Mammas et al., 2014). The patient described here did not have a history of human papillomatosis, and the symptoms lasted only for 14 days, thus no indication could be found that human papillomatosis was involved. It has also been suggested that HPV, particularly type 16, can play a role in oropharyngeal cancers (D'Souza et al., 2007); however, the clinical data from this patient gave no evidences of tumor.

We therefore concluded that HPV-A2619 does not have a specific pathogenic role in a detectable disease and our results are in agreement with other studies which reported the presence of HPVs in the respiratory tract (Kurose et al., 2004; Martinelli et al., 2012; Forslund et al., 2013; Mokili et al., 2013).

#### **SENSITIVE METAGENOMIC METHODS AND PATHOGEN DISCOVERY**

Thanks to the advances in molecular biology – especially the development of next generation HTS methods – virus discovery techniques became extremely sensitive and VIDISCA-454 (the method developed in our laboratory) is not an exception (de Vries et al., 2011, 2012). The load of HPV-A2619 was determined by real time PCR, and only 200 copies per 100 μl (input in VIDISCA) were measured. This sensitivity is remarkable and might be related to the nature of the virus. HPVs carry a double stranded DNA genome and their detection by VIDISCA-454 does not depend on reverse transcription and second strand synthesis, procedures which decrease the detection potential of RNA viruses.

With state of the art HTS based virus discovery techniques and the possibility to obtain such a huge number of sequences from a clinical sample, the identification of novel viruses became very effective, and nowadays the detection of low load viruses is not uncommon since minority nucleic acids can be efficiently sequenced (Mokili et al., 2012; Cotten et al., 2014). These methods are exclusively based on random sequencing of nucleic acids and they do not provide further information beyond the recognition of viral genetic material in a clinical sample, which does not necessarily correlate with the presence of a pathogen in the sample. In fact, some of these sequences might be derived from contaminating viruses introduced during the sampling or the sample processing procedures (Naccache et al., 2013). It is therefore important to confirm the presence of the recognized viruses in the original sample and to investigate the clinical relevance of every discovery to avoid drawing false conclusions.

With the development of HTS techniques the experimental time needed to discover novel viruses has dramatically reduced. However, targeted experiments and careful evaluation of all available data are essential to assess the relevance of every finding and to determine the role of every identified virus, since – as we show here – the mere identification of a virus is not sufficient to prove its involvement in a disease. This is especially true when dealing with body parts which are contiguous with the external environment – like the respiratory tract – and therefore more subjected to the presence of bystander or non-host-specific microorganisms.

#### **AUTHOR CONTRIBUTIONS**

Marta Canuti and Martin Deijs performed the experiments, participated to study design and wrote the paper. Seyed M. Jazaeri Farsani, Melle Holwerda, Maarten F. Jebbink, and Michel de Vries performed the experiments. Saskia van Vugt, Curt Brugman, Theo Verheij, Christine Lammens, Herman Goossens, Katherine Loens, and Margareta Ieven provided the clinical samples and participated to the study design. Lia van der Hoek participated to the study design, supervised the study and helped with the manuscript preparation. All authors critically revised the manuscript and approved the final version.

### **ACKNOWLEDGMENTS**

This study was supported by funding from the European Community's Sixth Framework Programme EC grant agreement number LSHM-CT-2005-518226 under the project GRACE and European Community's Seventh Framework Programme EC grant agreement number 223498 under the project EMPERIE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank all people involved in the GRACE Primary Care Network for the opportunity to conduct this study.

### **REFERENCES**

overview in adulthood and childhood. *Infect. Genet. Evol.* 21, 220–226. doi: 10.1016/j.meegid.2013.11.002

mmunity-based birth cohort study. *Pediatr. Infect. Dis.* 27, 100–105. doi: 10.1097/INF.0b013e31815922c8

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

#### *Received: 15 April 2014; paper pending published: 17 June 2014; accepted: 23 June 2014; published online: 08 July 2014.*

*Citation: Canuti M, Deijs M, Jazaeri Farsani SM, Holwerda M, Jebbink MF, de Vries M, van Vugt S, Brugman C, Verheij T, Lammens C, Goossens H, Loens K, Ieven M and van der Hoek L (2014) Metagenomic analysis of a sample from a patient with respiratory tract infection reveals the presence of a* γ*-papillomavirus. Front. Microbiol. 5:347. doi: 10.3389/fmicb.2014.00347*

*This article was submitted to Virology, a section of the journal Frontiers in Microbiology. Copyright © 2014 Canuti, Deijs, Jazaeri Farsani, Holwerda, Jebbink, de Vries, van Vugt, Brugman, Verheij, Lammens, Goossens, Loens, Ieven and van der Hoek. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## A metagenomic approach to characterize temperate bacteriophage populations from Cystic Fibrosis and non-Cystic Fibrosis bronchiectasis patients

*Mohammad A. Tariq1†, Francesca L. C. Everest 1†, Lauren A. Cowley2, Anthony De Soyza3,4, Giles S. Holt 1, Simon H. Bridge1, Audrey Perry3, John D. Perry3, Stephen J. Bourke5, Stephen P. Cummings 1, Clare V. Lanyon1, Jeremy J. Barr 6 and Darren L. Smith1 \**

*1 Faculty of Health and Life Sciences, University of Northumbria at Newcastle, Newcastle Upon Tyne, UK*

*2 Public Health England, London, UK*

*3 Freeman Hospital, Newcastle Upon Tyne, UK*

*4 Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, UK*

*5 Royal Victoria Infirmary, Newcastle Upon Tyne, UK*

*6 Department of Biology, San Diego State University, San Diego, CA, USA*

#### *Edited by:*

*Alejandro Reyes, Universidad de los Andes, Colombia*

#### *Reviewed by:*

*Steven Ripp, University of Tennessee, USA Samuel Schwartz Minot, Signature Science, LLC, USA*

#### *\*Correspondence:*

*Darren L. Smith, Applied Sciences, University of Northumbria, Ellison Building, EBD222, Newcastle Upon Tyne NE1 8ST, UK e-mail: darren.smith@ northumbria.ac.uk*

*†Co-lead author's: Mohammad A. Tariq and Francesca L. C. Everest.* *Pseudomonas aeruginosa* (Pa), normally a soil commensal, is an important opportunistic pathogen in Cystic Fibrosis (CF) and non-Cystic Fibrosis Bronchiectasis (nCFBR). Persistent infection correlates with accelerated decline in lung function and early mortality. The horizontal transfer of DNA by temperate bacteriophages can add gene function and selective advantages to their bacterial host within the constrained environment of the lower lung. In this study, we chemically induce temperate bacteriophages from clonal cultures of Pa and identify their mixed viral communities employing metagenomic approaches. We compared 92 temperate phage metagenomes stratified from these clinical backgrounds (47 CF and 45 nCFBR Pa isolates) using MG-RAST and GeneWise2. KEGG analysis shows the complexity of temperate phage accessory gene carriage increases with duration and severity of the disease. Furthermore, we identify the presence of Ig-like motifs within phage structural genes linked to bacterial adhesion and carbohydrate binding including Big\_2, He\_Pig, and Fn3. This study provides the first clinical support to the proposed bacteriophage adherence to mucus (BAM) model and the evolution of phages interacting at these mucosal surfaces over time.

**Keywords:** *Pseudomonas aeruginosa***, temperate bacteriophage, cystic fibrosis, non-Cystic fibrosis bronchiectasis, mixed phage populations**

### **INTRODUCTION**

Chronic respiratory diseases are associated with about 4 million deaths globally per annum (WHO1 ). Cystic Fibrosis (CF) is a rare but well documented inherited chronic respiratory disease that is characterized by chronic bacterial infections of the lung (Mall and Boucher, 2014). Non-Cystic Fibrosis bronchiectasis (nCFBR) is usually associated with an older population; it is an abnormal and irreversible dilation of the lower bronchi. Unifying features between CF and nCFBR are the propensity for *Pseudomonas aeruginosa* (Pa) to be an opportunistic pathogen and abnormal mucus retention in the lower respiratory tract.

Pa is challenging to study at the genome level due to the presence of multiple genomic islands and a burgeoning accessory genome that may well correlate with its opportunistic nature. Opportunistic bacteria colonize the lungs of patients with chronic respiratory disease and utilize the nutrient rich mucus lining of the lower airways allowing for bacterial replication and evolution to occur in an often deteriorating microenvironment (Nelson et al., 2010; Hauser et al., 2011; Rudkjobing et al., 2011). Other bacteria that are commonly isolated from chronically infected lung include; *Staphylococcus aureus, Haemophilus. influenzae, Stenotrophomonas maltophilia, Achromobacter xylosoxidans,* whilst *Burkholderia cepacia* complex and Pa are descriptive of the CF lung and are linked to poor clinical outcomes including lowered lung function (Lipuma, 2010). Multiple phages have been previously identified in Pa isolated from the CF lungs (Winstanley et al., 2009).

Bacteriophages can be either classed as lytic or temperate. Lytic phages upon entry into their host bacterium rapidly propagate leading to cell lysis. Importantly, lytic phages do not become integrated into the bacterial chromosome; this is in contrast to temperate phages which upon entry into the cell integrate into the host genome as a prophage. Temperate phages infect their bacterial host and lay dormant within the bacterial host chromosome until they are induced from their host, forming an infective phage particle. It has been noted that phages typically outnumber bacteria by a factor of 10 (Fineran et al., 2009). Previous metagenomic

1WHO. Available online at: http://www*.*who*.*int/genomics/public/ geneticdiseases/en/index2*.*html#CF [Accessed February 20, 2014].

studies focusing on viruses have identified novel patterns associated with evolution and novel viral particles (Kristensen et al., 2010). In this study, temperate bacteriophages were induced from their bacterial host using Norfloxacin (Matsushiro et al., 1999).

The major focus of numerous genome studies is determination of the core phage genome architecture. This study uses a metagenomic approach to elucidate the depth, function and complexity of phages evolving in a constrained environment of the lower lung. Conventional genome assembly tools try to compile mixed communities into single phages as they try to match and overlay similarity of sequence composition. Here we employ Metagenomics Rapid Annotations based on Subsystem Technology (MG-RAST) to overcome the need to assemble single phages and focus on the accessory genomes functionality. Another advantage of using MG-RAST is to generate Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways; these allow for analysis of gene functionality via linking genetic information with higher order functional information (Kanehisa and Goto, 2000).

Here we focused on lysogenic phages, as they form an intrinsic part of the adaptation and evolution of bacteria (Bankevich et al., 2012). Temperate phages have been shown to carry a range of genes that can alter the fitness or pathogenicity of a bacterium. An example would be the ability to encode functional toxins in their host bacterium which in turn can increase the severity of disease and may influence its progression (Beddoe et al., 2010; Boyd et al., 2012; Dubreuil, 2012). Our focus was on phage accessory genes, which link temperate phages to bacterial adaptation and evolution in chronic lung infections. Phage accessory genes are understudied as they are small with no offered function; importantly these genes are usually shared between phages suggesting a conserved role in their biology or for subversion of their host (Smith et al., 2012). Here we compare clinical data and the complexity of phage-encoded accessory gene function to link to the pathophysiology of the chronic lungs in patients with CF and nCFBR. Metagenomic studies are beneficial for studying mixed viral communities as they utilize culture independent methods allowing for the observation of viral communities that lack a known propagating host and therefore, can be underrepresented in some studies. This is also a problem in bacterial studies with the inability to culture all strains in the laboratory so increasing the need for direct DNA sequencing methodologies that limit culture bias (Hugenholtz et al., 1998). Further complexity is added to mixed population genome assemblies as bacteria and viruses carry homologous genes with conserved order which can make them harder to separate bioinformatically (Drancourt et al., 2000; Boudewijns et al., 2006). Metagenomics can further be utilized to investigate pan-functionality in a sample compared to more traditional taxonomic approaches (Tringe et al., 2005), making it an ideal tool for *de novo* studies as it negates the need for previous knowledge of the sample (Roux et al., 2014).

Mucus forms the first line of defense for protection against pathogenic infection in the lung forming a physical barrier between the center of the airways and the underlying epithelial cells (Hansson, 2012; Barr et al., 2013). Mucus is composed mostly of mucin, a host produced glycoprotein but other macromolecules are also known to be present (Kim and Ho, 2010). The Bacteriophage Adherence to Mucus (BAM) model proposes that lytic phages adhere to carbohydrate residues present within the mucus layer, and provide a layer of immunity to incoming bacteria (Barr et al., 2013). The BAM model is mediated by structurally displayed carbohydrate-adherence domains, such as the immunoglobulin (Ig)–like domain present on the capsid of phage T4 (Hoc). Structural proteins with associated Ig-like domains have been found in approximately 25% of the sequenced dsDNA phages, demonstrating their ubiquity and their potential importance in aiding phage survival (Fraser et al., 2007). Such structural domains have been seen to be indispensable for phage propagation in laboratory settings as they mediate interactions between the phage and its host cell (McMahon et al., 2005; Fraser et al., 2007).

Here we investigate whether the temperate phages isolated from the mucus rich environments of CF and nCFBR patients' lungs support the BAM model and its clinical relevance. We utilize the BAM model to propose a different strategy for phages to disseminate across their host These observations led to the hypothesis that these domains may aid in both the adsorption of phages to their bacterial hosts and the mucus layer under certain environmental conditions (Fraser et al., 2007). We hypothesized that temperate phages may use the mucus barrier as a way of infecting incoming bacteria which may drive gene exchange and add another level of adaptation and evolution. It also may be a way of increasing genetic heterogeneity in a population of bacteria in late stage chronic lung infections that are traditionally thought to be somewhat clonal. We compare the inducible temperate phages of Pa found in the lungs of patients with CF and nCFBR using a metagenomic approach and study the complexity of putative functional traits the phage accrue through their continual adaptation and evolution in the chronic lower lung.

### **MATERIALS AND METHODS**

### **BACTERIAL ISOLATES AND MEDIA**

The Pa isolates in this study originate from clinical isolates collected at the Freeman Hospital and the Royal Victoria Infirmary; Newcastle Upon Tyne Hospital Trust, UK (10 pediatric CF isolates, 37 adult Cystic Fibrosis (CF) isolates, 17 *<* 10 year clinical diagnosis nCFBR isolates and 28 *>* 10 year nCFBR isolates). All bacterial cultures were propagated in Luria Broth (LB) media (Sigma Aldrich, Gillingham, UK), CaCl2 was added to Soft Agar [0.4% high clarity agar (Lab M Limited, Heywood, UK) and 0.01 M CaCl2 (Sigma Aldrich)] to promote phage adsorption; the cultures were incubated at 37◦C for 18 h (+200 rpm if liquid culture). Full ethical approval has been given for this work (REC reference: 12/NE/0248).

#### **PROPHAGE INDUCTION AND RE-INFECTION**

Lysogenic bacteriophages were chemically induced from bacterial isolates. In brief, overnight cultures were sub cultured 0.2% (v/v) (10 mL LB Broth, 0.01 M CaCl2). The phages were induced by stressing the bacterium with fluoroquinolone antibiotic, Norfloxacin (1μg.mL−1) (Sigma Aldrich) for 1 h (37◦C, 200 rpm). The culture containing norfloxacin was diluted (1:10) to limit the cytotoxic effect of the drug, so allowing for the cascade of phage induction to occur (Matsushiro et al., 1999). Phage lysates were filtered through a 0.22μM filter (Scientific Laboratory Supplies, Hessle, UK) and stored at 4◦C for *<*1 week. Phage lysates were also utilized to identify whether a phage from the lysate had the ability to re-infect the originating bacterial host. The lysates were spotted (10μl) in dilution onto a lawn of originating host Pa cultured in 0.4% (w/v) agar + Luria Broth, overlaid on LB agar. Dilution identified plaques over possible pyocin production.

### **PHAGE DNA ISOLATION**

Bacterial chromosomal DNA was attenuated using 1μL of TURBO DNAse and 1μL of RNAse Cocktail (Life Technologies Limited), prior to incubation at 37◦C for 30 min followed with heat inactivation at 65◦C and 0.5 M EDTA. NORGEN Phage DNA Isolation Kits (Geneflow Limited, Lichfield, UK) were used to purify viral DNA, in accordance with manufacturer's protocol. The NORGEN phage DNA isolation kit was chosen due to its optimal yield of phage DNA compared to the QIAGEN QIAmp MinElute Viral Spin Kit, Chelex extraction, and PEG8000 purification (Sambrook et al., 1989) whilst limiting bacterial chromosome contamination. A low level of bacterial chromosomal contamination was determined by PCR for the 16S rRNA gene but it was negated bioinformatically using the Khmer toolkit (Muyzer et al., 1993).

#### **NEXT GENERATION DNA SEQUENCING**

The Illumina Nextera XT (Illumina, Saffron Waldon, UK) library preparation kit was used to prepare and multiplex the isolated phage DNA for next generation sequencing on the Ilumina MiSeq platform. A 2 × 250 cycle V2 kit was used for the loading and running of the sample. The DNA samples were diluted to 0.2 ng/μL (Qubit 2.0 DS HS DNA Kit [Life Technologies Limited]) prior to normalization and pooling. Paired end sequencing reads where provided as FASTQ files (NU-OMICS, Northumbria University at Newcastle, UK) and subject to downstream analysis.

### **BIOINFORMATIC TOOLS**

### **RANDOMIZING DNA READS USING VELVET V1.2.10**

Velvet *de novo* genome assembler package shuffleseq.pl was used to randomly shuffle the FASTQ sequences to limit bias. The shuffled sequence output file was directly pipelined into the Khmer toolkit. Command line script can be found in Supplementary Material S6.

### **KHMER TOOLKIT**

Khmer uses a probabilistic Bloom filter to separate out k-mer abundance and to group accordingly. This is achieved by creating hash tables that store specific k-mers and their counts using the default settings. The toolkit was utilized to remove very lowlevel bacterial contamination from the viral sequence data. The Khmer histogram clusters low abundance data and poor sequence data that would be linked to any residual bacterial chromosomal DNA where we can use the defining python script to select abundant viral k-mer sequencing data (Brown et al., 2012). Command line script can be found in Supplementary Material S6. For each individual sequence file we assessed the out.hist files graphed in excel and manually remove the error k-mer peak. These output files then can be pipelined to MG-RAST once the data is renamed using "Rename Sequences" v 0.0.11 (Blankenberg et al., 2010).

#### **RENAMING FASTA FILES**

Following the separation of the raw data into separate sequence files via Khmer, the files were converted from FASTQ to FASTA using a python script (Khmer package). Sequences were renamed numerically using "Rename Sequences" v 0.0.11 (Blankenberg et al., 2010). These 92 viromes where then uploaded to MG-RAST. From the 92 files and sequence cleanup 10 files had under the threshold of data allowed for uploading onto MG-RAST. However, we still assembled each of these samples using the three assemblers and used them to search for any putative Ig-like domains.

#### **MG RAST**

The KEGG generator function was used in order to show possible differences in the biochemical pathways between the phage isolated from the 2 clinical origins and stratified in the methods section. The maps were generated with the MG-RAST default setting: 60% sequence similarity of 15 amino acids. The hierarchical classification system tab was used in order to generate Principle Component Analysis (PCoA) on the samples relating to their functionality, the data was normalized and drawn according to the Minkowski distance with MG-RAST default settings.

#### **THREE-WAY GENOME ASSEMBLY COMPARISON**

Before we could search the sequence data for putative carbohydrate binding motifs using GeneWise2 we perform a three-way assembly study using SPAdes v 3.1.0, Velvet optimizer v 2.2.5 and IDBA-UD v 1.1.1. An overview of the assemblies is provided in Supplementary S5 that details each assembly comparison showing N50 scores, number of contiguous sequences derived and the largest contig size. **Figure 5A** shows the ability to detect Ig-like domains compared between the assemblers.

#### **HMM/PFAM DATABASE SEARCHES USING GENEWISE2 V2.2.0**

GeneWise2 was used to search a database of 92 sequence files against the Pfam database of 40 amino acid based HMMs as shown in **Table 1**. HMMER v 3 was used to revert the HMMs from version 3 to version 2, so GeneWise2 could recognize these files. GeneWise2 algorithms 6:23 and 21:93 were used and comparisons drawn with Jalview v 2 (Waterhouse et al., 2009). The gene locations of the resulting positive results were compared to the putative ORF associated with the GeneWise2 identification.

### **RESULTS**

Putative mixed viral communities were induced from a crosssectional panel of 92 *Pseudomonas aeruginosa* (Pa) bacterial isolates, 47 isolated from CF patients and 45 nCFBR stratified further by patient clinical information detailed in the methods. Phage DNA was isolated away from bacterial chromosome and any low level remaining bacterial DNA was removed bioinformatically using Khmer toolkit as described both in the methods and Supplementary Material.

*They were individually selected as they encode putative carbohydrate binding domains.*

**FIGURE 1 | Total KEGG putative function identifications derived for phage lysates.** Each putative functional pathway is represented as a single identification regardless of the amount of times that the putative function may have been conferred in each of the clinical subgroups. The highest rates of identification are seen in the lysates that originate from adult CF patients whilst the lowest rates of identification are seen in the lysates from *<*10 year clinical diagnosis nCFBR patients.

#### **MG-RAST DERIVED KEGG PATHWAY ANALYSIS**

Eighty two from ninety two isolates were analyzed through MG-RAST and KEGG analysis due to 10 of the sequencing files having insufficient sequence data for analysis. The pathways that possibly confer putative function are shown in the Supplementary Data alongside the raw data generated from each KEGG pathway (S1 and S2 respectively). Using KEGG pathway analysis through MG-RAST we defined the "incidence" of DNA sequences with shared similarity with a known metabolic pathway stored in the KEGG database. An overview of the number of reads per sample is shown in Supplementary S3 whilst the KEGG derived EC values downloaded from the KEGG generator tab on MG RAST are shown in Supplementary Table S4.

Using KEGG pathway analysis **Figure 1** identifies the presence of different metabolic pathways that are stored in this database. We stratify this further using the clinical information detailed in the methods. We identify an increase in KEGG derived pathways that link to the duration of clinical disease in adult CF and *>*10 years nCFBR. However, nCFBR (*<*10 year clinical diagnosis) lysates have the lowest number of KEGG pathway incidences. There is an overall increase in the number of identifications as both diseases progress. This may show phage adaptation and accrual of genes that possibly aid the fitness of the bacterial host within this environment. **Figure 2** shows bar graphs demonstrating the incidence of KEGG identifications that have been stratified both by clinical etiology but also by the defining zones of the KEGG atlas. The KEGG pathway analysis identifies gene regions and links to functional pathways relating to metabolism

**FIGURE 2 | Incidence of amino acid similarity to KEGG functional pathways generated by MG-RAST.** These data are further stratified sub-etiology to; pediatric CF (ped CF), adult CF, *<* 10 year clinical diagnosis nCFBR and *>*10 year clinical diagnosis nCFBR. Each putative functional pathway is represented as a single identification regardless of the amount of times that the putative function may have been conferred in each of the clinical subgroups. These KEGG pathways can be used to confer possible functionality and difference between the disease states. A clear increase in the number of KEGG pathway identifications is linked to patient age regardless of disease etiology ("Glycan biosynthesis and metabolism," "Xenobiotic degradation," and "Mechanism of Cofactors and Vitamins"). This pattern is reversed in some panels with a reduction in

the number of identifications decreasing as the disease progresses regardless of etiology ("Metabolism of Terpenoids and Polyketides" and "Energy Metabolism"). When looking Nucleotide metabolism there is a decrease in the amount of identifications between *<*10 year clinical diagnosis nCFBR and *>*10 year clinical diagnosis nCFBR but an increase between ped CF and adult CF patients. In two panels ("Lipid Metabolism" and "Metabolism of other amino acids") there is also an increase in the number of identifications seen as nCFBR patients disease state progresses but a decrease in the number of hits as a CF lung deteriorates. When looking at the final panel ("Biosynthesis of other secondary metabolites") no similarities are detected for the CF and nCFBR patients regardless of progression of disease.

and signaling. Increases in functionality that can be linked to disease progression include; glycan biosynthesis and metabolism; xenobiotic degradation; mechanisms of cofactors and vitamins. An overall decrease in identifications was identified in pathways relating to; metabolism of terpenoids and polyketides, and energy metabolism. However, when looking at some areas of the KEGG atlas there are differences between the CF and nCFBR lysates. Lipid, carbohydrate and amino acid metabolism increases as nCFBR progresses where the opposite is apparent for CF patients. The reverse of this trend is observed in hits relating to nucleotide metabolism, with a decrease in hits in nCFBR patients compared to an increase in CF patients. It is noteworthy that when comparing identifications relating to the biosynthesis of secondary metabolites it is clear that there are no hits in a CF phages background.

### *MG-RAST derived PCoA*

Principal component analysis (PCoA) generated by pan differences in functionality (**Figure 3**) illustrates that the phage samples show unrelated functionality and that there is no observable link between the phage functionality on the PCoA. This suggests that even though differences in function are seen when looking at the KEGG atlas (**Figures 1**, **2**); these differences are not discrete enough to lead to separation on the PCoA plot and thus show that not all phage carry all traits.

### *Phages ability to Superinfect Pa*

A large number of phages that were chemically induced in a previous study from the 94 isolates have the capability of re-infecting their originating bacterial host (**Figure 4**). Stratification according to disease origin has been shown in **Figure 4A** where 49% of nCFBR related phages had the ability to re-infect the originating host compared to 66% of the CF Pa induced phages. In **Figure 4B** further stratification illustrates that 16% of pediatric CF Pa phages within these samples and 84% of the adult samples have the ability to re-infect. This trend is also seen in nCFBR patients with 30% *<*10 years clinical diagnosis and 70% *>*10 years clinical diagnosis able to re-infect their originating host.

**FIGURE 4 | Graphical representation of the percentage of phages that are capable of re infecting their originating host (% inf. ori. host = % infection of originating host). (A)** Shows the percentage of phage lysates from both nCFBR and CF, with CF phage having greater ability to infect their originating host. **(B)** shows the stratification of these rates shown in **(A)**, illustrating that this trait is acquired over time in phages isolated from both clinical etiologies.

### *Presence of Ig-like domain Big\_2*

The frequency of Ig-like binding domains is described in **Figure 5**. It illustrates that the frequency of Big\_2 domains increases alongside the longevity of disease. This has been calculated as a percentage of the total number of phages for each of the clinical strata, which have been seen to contain a Big\_2 domain (CF: pediatric CF phages 30%, adult CF phages 40%; nCFBR: *<*10 years clinical diagnosis 6%, *>*10 years clinical diagnosis 32%). This work is unique as these domains have not been observed on phage genomes to this level previously, especially on phages with known clinical origins.

### *Identification of Ig-like binding domains*

Big\_2 domains were only identified with both GeneWise2 6:23 and 21:93 algorithm and these were seen in 28 of the 92 mixed phage samples. All bit similarity scores were above the recommended cut-off of 25, were considered significant for gene prediction (Fraser et al., 2006). The domain architecture found in these samples is shown in **Figure 6A**. It was seen that the Big\_2

**FIGURE 5 | (A)** Describes the total number of Ig-like domains identified using Genewise2 subsequent to assembly by SPAdes, IBDA-UD, and Velvet Optimizer. **(B)** Offers the percentage of the total number of Pa isolates from the 4 clinical groups with one or more Ig-like domain. **(C)** Focuses on the percentage incidence of the Big\_2 domains which attracted the highest bit scores of detection using GeneWise2.

protein. **(B)** Shows the sequence alignments of the Ig-like domains; Big\_2, Big\_3, Fn3, Glyco\_hydro\_2, He\_Pig and CelD\_N found in phage proteins. The sequence alignment of Big\_2 is represented along with 4 variations that were

domains were found in duplicate and associated with the major tail protein. It was also seen using GeneWise2 algorithm 21:93, where a double He\_Pig domain presented in a large subset of the Pa phages as a double motif in putative structural genes flanking a putative minor phage tail protein. The He\_Pig double domain was seen in 42 of the phage samples, over 70% of which have the Glyco\_hydro\_2 domain was seen in only one phage sample (*>* 10 clinical diagnosis nCFBR isolate). The He\_Pig double domain was seen in 42 of the phage samples, 70% of which have the amino acid alignment shown above.

amino acid alignment shown in **Figure 6B**. All the other hits to the He\_Pig domain showed poor bit scores and were thus not aligned in **Figure 6B**.

When the GeneWise2 21:93 algorithm was used, it was seen that a Big\_3 domain was present overlapping (by 89 amino acids) the first Big\_2 domain which may link to a frameshift that has been described in other Ig-like binding motif architecture (Fraser et al., 2006). This Big\_2, Big\_3 combination trend was seen in all of the samples, except for four where the algorithm failed to report any significant similarity. We also identified another domain in 10 of the samples called Fn3. The domain alignments for Big\_3, Fn3, Big\_2, glycol\_hydro\_2, CelD\_N, and He\_Pig are shown in **Figure 6B**. The Glyco\_hydro\_2 domain was identified but not associated with a putative structural protein. Upon utilizing a BLASTn search the Fn3 domain was located in a phage tail assembly protein. Similarly the ORF for Big\_2 was also identified in a structural major tail protein gene, this time the major tail 2 protein. Other Ig-like domains have been identified but they all overlapped the first Big\_2 domain including; PPC, Peptidase\_C25\_C, Big\_3, and Big\_4 all had too low bit scores.

### **DISCUSSION**

This study reports the first use of metagenomic approaches to identify the inducible temperate bacteriophages isolated from single clonal cultures of *Pseudomonas aeruginosa* (Pa) colonizing the lungs of patients with CF and nCFBR. These data were further compared to the clinical information provided for each patient sub-group. A noteworthy observation is the evolution in complexity of the viral population linking to possible functions that aid bacterial fitness and therefore, viral sustainability in chronic lung infections. Key findings are the high frequency of Ig–like motifs found in the temperate phages of Pa with known clinical origin and also the increase in complexity of putative phage function when adapting to the progressive disease state of the lower lung.

When analyzing the metagenomic results generated via KEGG analysis through MG-RAST, it is clear to see an increase in complexity of function and possibly the level of adaptation occurring between the phage in accordance with either patient age or colonization time with Pa (**Figures 1**, **2**). Using KEGG we also observed specific functional trends encoded by temperate phages that are both similar and disparate between the disease states. An increase is seen in the number of identifications for glycan biosynthesis and metabolism; this is possibly due to these functions being associated with cell wall synthesis in the bacteria and possibly associated with inflammation in the lung. The CF lung also contains a large amount of human produced mucins which are covered in glycans, so it is possible that the phage are transporting functions relating to glycan biosynthesis in order to increase the degradation of these mucin's and thus promote bacterial growth in the CF lung. Phages therefore, may utilize these functions in order to survive, by driving further inflammation in the lower lung environment offering preferential selection for the bacterium. When focusing on certain subsections such as "carbohydrate metabolism" and "nucleotide metabolism," apparent differences are seen between some of the disease sub-groups and these may be caused by alteration in metabolic precursors within the lung environment where addition by the phage offers an alternate pathway of metabolism. It is surprising to see the low level of association with "energy metabolism" in adult CF phage metagenomes and in *>*10 year clinical diagnosis nCFBR phage, but it may be possible that this is due to colonizing bacteria, in later stages of disease progression, may have evolved the perfect metabolism to survive and thus this relates to gene subtraction over addition. This also may be linked to the propensity of these Pa isolates to sustain in a biofilm.

The increase in the ability of certain phage communities to encode functionality that enables bacteria to degrade xenobiotic compounds is illustrated to evolve alongside the bacterium. It is notable that this also links to the previous reports of increasing antibiotic resistance in Pa isolated from the chronic lung (Winstanley et al., 2009). This pattern of increasing incidence of putative function also correlates to the increasing timeline of these diseases. This is illustrated by increases in incidence of metabolic pathways of cofactors and vitamins that have a role in generating biological activity. It may be possible that these cofactors are being encoded by the phage to aid bacterial survival and fitness. It must also be taken into account the apparent lack of these factors early on in these diseases may show a possible evolutionary timeline. The sizeable work undertaken here shows that adaptation of phage communities is apparent, with the amount of bacterium and possible number of phages offering confidence. This notable observation emphasizes the complexity and detailed nature of chronic lung infections and how phage evolution over time may affect the phenotype of Pa which in turn will have an overall impact on disease progression.

Bacteriophages evolve strategies alongside their bacterial host that promote infection, propagation and offer fitness to their host range. The inflamed lung is rich in mucus and the Pa isolated are variable in phenotype with some Pa expressing a mucoid surface. Therefore, it was pertinent to compare the BAM model of lytic phages as it may offer a way that temperate phages infect and transduce across their host range in the lower lung. Initially in this investigation we focused our attention on specific carbohydrate binding domains including Bacteriodetes Associated Carbohydrate Often N–terminal (BACON) (Mello et al., 2010) as this has been shown to be involved in the BAM model (Dutilh et al., 2014). However, searching Pa phage metagenomes using GeneWise2 identified no BACON domains in the panel of 92 phage samples. This does not refute the BAM model in this setting as the phage may bind to the Pa bacterial host via another carbohydrate or glycoprotein. Out of the 92 isolates and their associated phage, a Big\_2 domain was observed in at least one of the phage's isolated from various clinical backgrounds (3 pediatric CF isolates, 15 adult CF isolates, 1 *<* 10 year clinical diagnosis nCFBR isolates and 9 *>* 10 year clinical diagnosis nCFBR isolates). When looking at the presence of the He\_Pig motif it was identified that its occurrence was equal in both etiologies with a higher propensity in adult CF phage and *>*10 year Pa colonization nCFBR phage (17 and 14 respectively), putatively showing a role in phage adaptation. There were hits for the He\_Pig domains in both pediatric CF phage (4) and *<*10 year clinical diagnosis nCFBR phage (7). Importantly we use three bioinformatics packages to assemble the DNA of each phage metagenome. We report that SPAdes and IBDA-UD are fairly comparable in performance, although we identified higher numbers of Big\_2 domains using SPAdes. In addition Velvet optimizer yielded the poorest assembly when specifically targeting Ig-like domains with GeneWise2. Importantly though the Velvet derived assembly contained an Ig-domain (PF00047) that both SPAdes and IBDA-UD failed to assemble. We would therefore recommend utilizing all 3 assemblers for future studies in this developing field.

Ig-like domains have been identified on approximately 25% of all sequenced *Caudovirales* genomes, there are three distinct families which are only identified in *Caudovirales* phage (Big\_2, I-Set, and fn3) (Fraser et al., 2007). These domains on *Siphoviridae* and *Podoviridae* are located on Major Head, Major Tail and Tail Fiber proteins whilst in *Myoviridae* they are located on HOC, Fibritin and Baseplate proteins (Fraser et al., 2007). The lytic T4 phage in the BAM model shows the phage associating with mucus via the head HOC protein, this is seen with Electron Microscopy (EM) as the domain protrudes from the surface of the phage (Crusoe et al., 2010). Ig–like domains have been identified in the tail tube protein of *E. coli* bacteriophage λ, the exact reason for these Iglike domains in λ is not fully known but when these domains are truncated, the phage has been seen to become more temperature sensitive (Katsura, 1981; Pell et al., 2010). These Ig–like domains may have accessory roles in bacterial infection rather than essential roles but their ubiquitous nature does potentially indicate there is an evolutionary advantage for the phage containing these domains (Pell et al., 2010; Barr et al., 2013). We show that Pa phages assembled in this study have a double Big\_2 domain. The He\_Pig domain was also seen in a double motif which flanked a putative minor phage tail protein (**Figure 6**). He\_Pig is an Ig-like domain that is found in hemagglutinin and cell surface proteins, so it is possible that this domain is involved in the BAM model. The major sequence variations seen between the 4 types of the double Big\_2 motif were found in the second Big\_2 domain, **Figure 6B**. All these domains had a bit score above the proposed score of 25 and had a gap ranging from 18 to 6878 bp.

The BAM model reports on the role of the capsid-displayed, Ig-like protein domain of phage T4, and suggests a mechanism for its adherence to mucus. The putative carbohydrate-binding domains identified in this study appear to be associated with phage tail protein structures, rather than capsid domains. The presence of Ig-like domains therefore, on the phage isolates may indicate that the BAM model is functional for temperate phage but as a mode of infection and propagation rather than phage mediated immunity. We determine that rapid evolution may be making the overall diversity of phage Ig-like domains very large and so this may hinder the detection of these domains with GeneWise2 (Fraser et al., 2006) hence our suggestion of utilizing multiple assembly software. When comparing the clinical data associated with the bacterial isolates there is no correlation between the detection of Ig-like domains and the severity of disease.

This metagenomic project has indicated that the phage isolated from Pa derived from CF and nCFBR patients can associate with carbohydrates either on the bacterial host cells or in the mucus via Ig–like domains. When looking at the samples containing a Big\_2 domain and/or a He\_Pig domain, it was seen that they were more common in adult CF phage and *>*10 year clinical diagnosis nCFBR phage, possibly indicating that the presence of these domains has an evolutionary advantage for the phage's longevity in the chronic lung. This is the first time that this level of frequency has been observed for Ig–like domains in clinical isolates. There are multiple pathways which the Pa phage may utilize in order to adapt and offer fitness to its Pa host in the chronic lung environment (**Figure 7**). Through these data we can propose possible strategies the phages have adopted in order to sustain the ability to infect and propagate within

the lower lung, alongside etiological and evolutionary difference in the bacterial host. We have previously determined that temperate phages of Pa have high levels of spontaneous induction, with elevated levels being observed when Pa is in the early rather than mid-exponential growth phase. We hypothesize that phage are released from their host cell early to avoid becoming encased in a potential biofilm and have vegetative cells to infect. However, some of the phage that contains the Ig–like domains may aid adsorption to the cell from which they originate, or aid attachment and infection of other cells in their direct environment. Addition of traits such as adherence to the mucus lining the lower airways in order to increase their longevity in the chronic lung environment is also pertinent as an adaptive strategy.

This project has used metagenomics to show the array of adaptive mechanisms phage communities accrue over time in the lung environment. This work highlights the novel nature of metagenomics to understand complex communities without the need for a sensitive bacterial host or the necessity to enrich bacteriophage numbers and culture independence. We look at the "total arsenal" of a mixed phage community being induced from a clonal population and look at their possible impact on their next host in terms of infection and subversion. We map the effect that these mixed phage communities have on the functionality of their host or the strategies including BAM that have evolved over time. Using single phage lysates would not represent the complexity of the phage communities from each bacterium and their infection strategies in the chronic lung environment.

In conclusion, this study characterizes the inducible temperate phages found in Pa isolates sampled from the lungs of patients with CF and nCFBR. We provide the first evidence of how the complexities of the phage genomes possibly adapt over time and acquire accessory genes linking to key metabolic and signaling processes that may aid phage survival and bacterial fitness within the chronic lung. This study reports for the first time, to the best of our knowledge, the identification of multiple Iglike domains on Pa temperate phages structural genes with such high frequency. There are increasing concerns over progressive anti-microbial resistance and the slow development pipeline for new antibiotics, so there is a real need for a developing novel paradigms and methods to overcome or further understand these challenges. These data offer temperate phages as a possible target in the resistance/ persistence pathways. Furthermore, they raise an additional clinical concern - could phage cross infection be a new challenge?

#### **ACKNOWLEDGMENTS**

We would like to acknowledge the sequencing facility NU-OMICS based at Northumbria University at Newcastle, UK). We are grateful to the National Institute for Health Research (NIHR) Local Clinical research network North East and North Cumbria for research nurse support. We would also like to thank all the nurses based both at the Freeman Hospital and Royal Victoria Infirmary, Newcastle Upon Tyne, UK for all their help collecting the Pa isolates involved in this investigation. We would also finally like to thank all of the patients that kindly agreed to participate in this scientific study.

### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www*.*frontiersin*.*org/journal/10*.*3389/fmicb*.*2015*.* 00097/abstract

### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 12 December 2014; accepted: 26 January 2015; published online: 18 February 2015.*

*Citation: Tariq MA, Everest FLC, Cowley LA, De Soyza A, Holt GS, Bridge SH, Perry A, Perry JD, Bourke SJ, Cummings SP, Lanyon CV, Barr JJ and Smith DL (2015) A metagenomic approach to characterize temperate bacteriophage populations from Cystic Fibrosis and non-Cystic Fibrosis bronchiectasis patients. Front. Microbiol. 6:97. doi: 10.3389/fmicb.2015.00097*

*This article was submitted to Virology, a section of the journal Frontiers in Microbiology.*

*Copyright © 2015 Tariq, Everest, Cowley, De Soyza, Holt, Bridge, Perry, Perry, Bourke, Cummings, Lanyon, Barr and Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## The human urine virome in association with urinary tract infections

### *Tasha M. Santiago-Rodriguez 1, Melissa Ly1, Natasha Bonilla2 and David T. Pride1,3\**

*1 Department of Pathology, University of California, San Diego, San Diego, CA, USA*

*2 Department of Biology, San Diego State University, San Diego, CA, USA*

*3 Department of Medicine, University of California, San Diego, San Diego, CA, USA*

#### *Edited by:*

*Katrine L. Whiteson, University of California, Irvine, USA*

#### *Reviewed by:*

*Hiroyuki Sakai, Kyoto University, Japan Lesley Ann Ogilvie, Alacris Theranostics GmbH/Max Planck Institute for Molecular Genetics, Germany*

#### *\*Correspondence:*

*David T. Pride, Department of Pathology, University of California, San Diego, 9500 Gilman Drive, MC 0612, La Jolla, San Diego, CA 92093-0612, USA e-mail<EMAIL_ADDRESS>
While once believed to represent a sterile environment, the human urinary tract harbors a unique cellular microbiota. We sought to determine whether the human urinary tract also is home to viral communities whose membership might reflect urinary tract health status. We recruited and sampled urine from 20 subjects, 10 subjects with urinary tract infections (UTIs) and 10 without UTIs, and found viral communities in the urine of each subject group. Most of the identifiable viruses were bacteriophage, but eukaryotic viruses also were identified in all subjects. We found reads from human papillomaviruses (HPVs) in 95% of the subjects studied, but none were found to be high-risk genotypes that are associated with cervical and rectal cancers. We verified the presence of some HPV genotypes by quantitative PCR. Some of the HPV genotypes identified were homologous to relatively novel and uncharacterized viruses that previously have been detected on skin in association with cancerous lesions, while others may be associated with anal and genital warts. On a community level, there was no association between the membership or diversity of viral communities based on urinary tract health status. While more data are still needed, detection of HPVs as members of the human urinary virome using viral metagenomics represents a non-invasive technique that could augment current screening techniques to detect low-risk HPVs in the genitourinary tracts of humans.

**Keywords: human microbiome, urinary tract infections, virome, virobiota, HPV, papillomavirus**

### **INTRODUCTION**

The presence of microbes inhabiting the human urinary tract has generally been associated with urinary tract infections (UTIs), but recent studies have demonstrated that the urine has its own unique microbiota even in the absence of UTIs (Nelson et al., 2010; Siddiqui et al., 2011; Wolfe et al., 2012). Many of these microbiota may not be culturable using conventional culture techniques, but the presence of a diverse microbiota in human urine has highlighted the need to understand whether there is a role for these communities of microbes in urinary tract health or disease. Healthy female urinary microbiota often include organisms also identified in the vagina (Fouts et al., 2012; Wolfe et al., 2012; Hilt et al., 2014), while healthy male microbiota may resemble the vagina, gut, and skin (Nelson et al., 2010; Dong et al., 2011; Fouts et al., 2012). There are numerous lower urinary tract symptoms that often do not have known infectious etiologies, so understanding whether these conditions may be influenced by disturbances to the urinary microbiota is of substantial interest.

UTIs are among the most common urological disorders in the health care settings, and are diagnosed using a combination of both symptoms and culture tests. *Escherichia coli* is recognized as the most common etiological agent of UTI, responsible for more than 80% of reported cases in females (Foxman and Brown, 2003), but there are numerous other microbes capable of causing UTIs (Stamm, 2002; Pour et al., 2011; Volkow-Fernandez et al., 2012). While a threshold concentration of 103 Colony Forming Units (CFU) per ml of urine accompanied with lower urinary tract symptoms is required for a UTI diagnosis, there are some pathogens that are not detected by standard microbiological culture techniques (Rubin et al., 1992). Disturbances to the urinary tract microbiota can be recognized in the presence of pathogens (Pearce et al., 2014), but it is not yet clear how long it takes for the urinary tract to recover its normal microbiota after both pathogen-mediated disturbances and antibiotic therapy.

While there is a substantial cellular microbiota indigenous to the human urinary tract, little is known about viruses. There are viral pathogens of the human urinary tract, such as adenovirus (Echavarria et al., 1998; Echavarria, 2008) and BK virus (Shinohara et al., 1993; Paduch, 2007; Egli et al., 2009), but these viruses generally are only pathogenic in subjects with compromised immune systems. There are other viruses such as Human papillomaviruses (HPVs) that inhabit human genital and rectal areas, and have previously also been found in urine (Pathak et al., 2014). Because HPVs can be associated with both warts and cancer in humans (Wylie et al., 2012), the diagnosis of its presence and particular subtypes associated with infection can be critical in the care of individual subjects who are infected. Previous studies have demonstrated that robust communities of cellular microbiota on human body surfaces are generally accompanied by communities of viruses. There are vast communities of viruses that inhabit the human oral cavity (Pride et al., 2012; Abeles et al., 2014; Ly et al., 2014), the human gut (Minot et al., 2011), the human respiratory tract (Willner et al., 2009), and human skin (Foulongne et al., 2012), which suggests that there likely would be communities of viruses that also are indigenous to the human urinary tract. One prior study identified a unique viral community in the subgingival crevice of individuals with severe periodontal disease (Ly et al., 2014), which suggests that viruses could play a role in health and disease. There are many subjects with lower urinary tract symptoms, with no known cellular microbial etiologies, yet viral communities in these subjects are completely unexplored (Enerly et al., 2013).