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A comprehensive method for amplicon-based and metagenomic characterization of viruses, bacteria, and… Uyaguari-Diaz, Miguel I; Chan, Michael; Chaban, Bonnie L; Croxen, Matthew A; Finke, Jan F; Hill, Janet E; Peabody, Michael A; Van Rossum, Thea; Suttle, Curtis A; Brinkman, Fiona S L; Isaac-Renton, Judith; Prystajecky, Natalie A; Tang, Patrick Jul 11, 2016

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METHODOLOGY Open AccessA comprehensive method for amplicon-based and metagenomic characterizationof viruses, bacteria, and eukaryotes infreshwater samplesMiguel I. Uyaguari-Diaz1,9, Michael Chan1, Bonnie L. Chaban2, Matthew A. Croxen1, Jan F. Finke3,4,5, Janet E. Hill6,Michael A. Peabody7, Thea Van Rossum7, Curtis A. Suttle3,4,5,8, Fiona S. L. Brinkman7, Judith Isaac-Renton1,9,Natalie A. Prystajecky1,9 and Patrick Tang10*AbstractBackground: Studies of environmental microbiota typically target only specific groups of microorganisms, withmost focusing on bacteria through taxonomic classification of 16S rRNA gene sequences. For a more holisticunderstanding of a microbiome, a strategy to characterize the viral, bacterial, and eukaryotic components isnecessary.Results: We developed a method for metagenomic and amplicon-based analysis of freshwater samples involvingthe concentration and size-based separation of eukaryotic, bacterial, and viral fractions. Next-generation sequencingand culture-independent approaches were used to describe and quantify microbial communities in watershedswith different land use in British Columbia. Deep amplicon sequencing was used to investigate the distribution ofcertain viruses (g23 and RdRp), bacteria (16S rRNA and cpn60), and eukaryotes (18S rRNA and ITS). Metagenomicsequencing was used to further characterize the gene content of the bacterial and viral fractions at both taxonomicand functional levels.Conclusion: This study provides a systematic approach to separate and characterize eukaryotic-, bacterial-, andviral-sized particles. Methodologies described in this research have been applied in temporal and spatial studies tostudy the impact of land use on watershed microbiomes in British Columbia.Keywords: Microbiome, Watersheds, Amplicon sequencing, Metagenomes, Metagenomics, Microbial fractionsBackgroundWater is the most basic and important natural re-source on our planet. While water is a renewable re-source, an expanding population and increased landuse create stress on the aquatic environment andthreats to water quality [1–3]. Although there aremany users of water, including animals, agriculture,and industry, the current emphasis for water qualityassessment is testing at the tap for the purpose ofhuman consumption rather than at the source watershed.Laboratory tests for fecal pollution use traditionalculture-based methods to detect bacteria such asEscherichia coli and total coliforms. Not only arethese methods slow and inaccurate due to differencesin enumeration strategies [4], but also they measureonly a fraction of the microorganisms in the sample[5, 6], missing important perturbations in the microbiota.Environmental or human disturbances can lead toperturbations in the watershed microbiome includingchanges in the endogenous microorganisms or theintroduction of human or animal fecal microbiota.These changes in community structure in combin-ation with environmental parameters may pinpoint tothe source of disturbance in water quality. Thus, a* Correspondence: ptang@sidra.org10Department of Pathology, Sidra Medical and Research Center, PO Box26999, Doha, QatarFull list of author information is available at the end of the article© 2016 Uyaguari-Diaz et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Uyaguari-Diaz et al. Microbiome  (2016) 4:20 DOI 10.1186/s40168-016-0166-1better understanding of the entire watershed micro-biome and sources of pollution in watersheds will becritical for assessing microbial community changesand associated threats to both ecosystem and humanhealth. Previous work has demonstrated that (i) nicheenvironments such as watersheds have unique micro-bial taxa signatures and (ii) microbial markers can beused to detect microbial pollution in water [7, 8].Still, the microbiomes of freshwater ecosystems havenot been as comprehensively studied as have otheraquatic environments such as marine ecosystems[9–11].Next-generation sequencing and culture-independentapproaches enable the detection of these perturbationsand the identification of biomarkers for pollution detec-tion and source attribution. There are multiple studiesthat have been conducted using culture-independent ap-proaches such as deep amplicon sequencing of the 16SrRNA gene and shotgun metagenomics to characterizebacterial communities and assess water quality and theoverall ecology in freshwater ecosystems [8, 12–15].While these studies have identified microbial signaturesof water quality, they are based upon the analysis of aspecific gene or microbial fraction (mainly bacteria) leav-ing other microbial fractions largely unexplored. For in-stance, plant viruses can be good markers for humanfecal contamination [16, 17] and bacteriophages can beused for microbial source tracking [18], demonstratingthat surveys of watershed microbiomes need to expandbeyond the typical bacterial 16S rRNA or single fractionstudies.To date, there is only one study that has characterizedthe different major microbial domains within the sameenvironmental sample (soil) [19]. The present study de-scribes a series of methods developed to more compre-hensively characterize freshwater microbial communities(eukaryotes, bacteria, and viruses) as a single unit. Watersamples from three non-interconnected watersheds insouthwestern British Columbia affected by different landuse (agricultural, urban, and protected sites) were con-centrated and fractionated by size using filtration thencharacterized using amplicon sequencing and metage-nomics (sequencing all the genetic material in a sample).Sequence-based metagenomics aimed for bacterial andviral communities, while deep amplicon sequencing in-cluded 18S rRNA gene, internal transcribed spacer (ITS)for eukaryotes, and 16S rRNA and chaperonin-60(cpn60) genes for bacteria. Due to the lack of a universalgene in viruses, amplicon sequencing was used to studyonly selected DNA and RNA viruses. Gene 23 (g23),which encodes the major capsid protein of T4-like bacte-riophages, has been widely used for phylogenetic studiesin different environments including aquatic environments[10, 20–23]. All known RNA viruses employ an RNA-dependent RNA polymerase (RdRp) for replication [24].As the largest group of RNA viruses, Picornavirales havebeen reported to infect a wide diversity of eukaryotes inaquatic environments [11, 25–28]; the RdRp gene fromthis order was selected to complement viral RNA meta-genomes in watersheds.Additionally, traditional bacterial markers of low waterquality such as total coliforms and E. coli were also in-cluded as part of this study. These series of approacheswere piloted in order to validate the laboratory methodsand define the baseline microbiota in three differentlyaffected watersheds of southwestern British Columbia.Ultimately, these methods will be applied in larger longi-tudinal studies to study the impact of land use on water-shed microbiomes and identify novel biomarkers ofwater quality.MethodsSample collectionForty-liter samples were collected in sterile plastic car-boys from three different watersheds in southwesternBritish Columbia, each representing a different land usetype (protected, agricultural, and urban). Samplingwithin each site was conducted in two to three locations(upstream, downstream, and at the “polluted” site).Table 1 summarizes the description of sampling sites.Land use was the primary determinant of watershed se-lection. Watersheds were selected in collaboration withprovincial agencies and scientists who have conductedresearch in these locations. A total of seven sampleswere collected within a 1.5-month period (March–April2012). Samples were pre-filtered in situ using a 105-μmspectra/mesh polypropylene filter (SpectrumLabs, Ran-cho Dominguez, CA) and kept at 4 °C for transport tothe laboratory for processing and storage within 2 h ofthe last sample collection. Ten liters of ultrapure (type1) water (Milli-Q, Millipore Corporation, Billerica, MA)was used as a filtration control.MetadataPhysico-chemical water quality parameters were mea-sured in situ using a YSI Professional Plus handheldmultiparameter instrument (YSI Inc., Yellow Springs,OH), a VWR turbidity meter model No. 66120-200(VWR, Radnor, PA) and a Swoffer 3000 current meter(Swoffer Instrumentsz, Seattle, WA). Total coliform andE. coli counts were determined using Colilert-24 (IDEXXLaboratories, Westbrook, ME). Chemical analysis in-cluded dissolved chloride (mg/L) and ammonia (mg/L)using automated colorimetric (SM-4500-Cl G) and phe-nate methods (SM-4500-NH3 G) [29]. Additionally, nu-trients (orthophosphates, nitrites, and nitrates) wereanalyzed following methods described by Murphy andRiley [30] and Wood et al. [31], respectively.Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 2 of 19Fraction separationMicrobial fractions were separated through a combinationof serial filtration approaches. Following pre-filtration insitu, water was filtered through a 1-μm Envirochek HV(Pall Corporation, Ann Harbor, MI) sampling capsule tocapture eukaryotic-sized particles, followed by filtrationthrough a 0.2-μm 142-mm Supor-200 membrane disc fil-ters (Pall Corporation, Ann Harbor, MI) to capture thebacterial-sized particles. To remove any remaining bacter-ial cells, the permeate was filtered again using a 0.2-μmSupor Acropak 200 sterile cartridge (Pall Corporation,Ann Harbor, MI) prior to tangential flow filtration (TFF).Viral-sized particles were concentrated to approximately450 mL as described by Suttle et al. [32] and Culley et al.[26], using a regenerated cellulose Prep/Scale TFFcartridge (Millipore Corporation, Billerica, MA) witha 30-kDa molecular-weight cutoff and nominal filterarea of 0.23 m2.Collection, fixation, and particle quantitation ofenvironmental samples using flow cytometry (FCM)Nine hundred and eighty-microliter aliquots of rawwater and 0.2 μm permeate, ultrafiltrate, and viral con-centrate were collected in duplicates during the filtrationprocess. Samples were fixed with 20 μl of 25 % glutaral-dehyde to reach a final concentration of 0.5 % glutaral-dehyde, inverted to mix, incubated at 4 °C in the darkfor 20 min, and then transferred to −80 °C freezer forstorage and further analysis. Abundance of viral andbacterial-sized particles were determined in duplicatewater samples using a FACSCalibur flow cytometer(Beckton Dickinson, San Jose, CA) with a 15-mW 488-nm air-cooled argon-ion laser as described by Brussaard(2004) [33]. Analysis of the FCM results was conductedusing CYTOWIN version 4.31 (2004) [34].Elution and concentration of microbial cells and viralparticlesMechanical procedures involving shaking and centrifu-gation were used to remove and concentrate microbialcells from the filters. Cells were washed with ×1phosphate-buffered solution (PBS) and 0.01 % TweenpH 7.4. Eukaryotic cells retained in the 1-μm EnvirochekHV capsules were eluted according to the manufacturer’sprotocol (Pall Corporation, Ann Harbor, MI). Eluates(~500 μL) of eukaryotic cells were dispensed into 1.7-mL microcentrifuge tubes and further precipitated bycentrifugation (15 min, 1500×g, 4 °C). Samples were keptat −80 °C for further nucleic acid extraction.To minimize the number of DNA extraction tubes,the 0.2-μm Supor membrane disc filter(s) was washedwith 15 mL of PBS to remove bacterial cells followed bycentrifugation (15 min, 3300×g, 4 °C). Aliquots of thewashed cell suspension were stored at −80 °C for furtherDNA extraction. Viral-sized particles eluted in 450 mLof sample required further concentration by ultracentri-fugation (4 h, 121,000×g, 4 °C). Viral-sized concentratepellets were resuspended in ×1 PBS to reach a final vol-ume of approximately 5 to 6 mL and incubated over-night at 4 °C with constant agitation (180 rpm). Anevaluation of ultracentrifugation as an approach to fur-ther concentrate viral-sized particles is also describedhere.Concentration of viral particles by ultracentrifugationValidation of ultracentrifugation as a method to isolatevirus-like particles was conducted using two DNA andRNA viruses isolated from clinical specimens at theBritish Columbia Centre for Disease Control (BCCDC):adenovirus (90–100 nm) and enterovirus (Coxsackie B2,~30 nm). Both viruses are routinely used as controls atTable 1 Description of sampling sitesWatershed SitenameAverage depth(m) at crosssectionAverage width(m) at crosssectionElevation fromthe sea level (m)Waterflow (m3/s)DescriptionUrbana, b UPL 0.17 1.26 119 0.06 At site of urban “pollution,” in residential area.UDS 0.14 2.68 8 0.29 Downstream of urban “pollution,” 1 km from UPL.Agriculturalc AUP 0.16 1.71 118 0.16 Upstream of agricultural “pollution.” Not affected byagricultural activity, with minimal housing nearby.APL 0.79 7.33 10 2.11 At site of agricultural “pollution.” AUP is upstream ofAPL, separated by 9 km. Multiple farms near this site.ADS 1.72 25.5 8 9.97 Downstream of agricultural “pollution.” ADS isdownstream of APL, 2.5 km away.Protected PUP 0.24 7.7 198 0.60 Upstream of drinking water reservoir in protectedwatershed.PDS 2.1 2.1 111 1.01 Downstream of PUP-fed reservoir, collected afterpassing through an 8.8 km pipe.aAverage distance between urban and agricultural watershed: 63 kmbAverage distance between urban and protected watershed: 101 kmcAverage distance between agricultural and protected watershed: 132 kmUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 3 of 19the BCCDC. An aliquot of 0.25 μl of adenovirus and en-terovirus control stocks was inoculated into A549 andprimary Rhesus monkey kidney cell cultures (DiagnosticHybrids, Athens, OH), respectively. Once the cytophaticeffect was 3+, cells were harvested in minimal essentialmedia (MEM) with 2 % fetal calf serum (Sigma-Aldrich,St. Louis, MO), separately brought up to a final volumeof 16 mL, and stored at −80 °C until later processing.For further cell lysis and release of viral particles, sam-ples were subjected to three rounds of freeze-thaw. Fol-lowing the final thaw, samples were filtrated through a0.2-μm Supor membrane syringe filters (Pall Corpor-ation, Ann Harbor, MI) and spiked with 435 mL ofMEM. The recovery efficiency was evaluated for bothsupernatant and concentrated pellets at different timepoints (1, 2, and 4 h) of the ultracentrifugation process(121,000×g, 4 °C). Virus concentrate pellets were incu-bated overnight at 4 °C on a shaker. At least duplicatealiquots from the different stages of the previously de-scribed processes were collected for flow cytometrycounts, nucleic acid extraction, and quantitation of vi-ruses in samples.Nucleic acid extraction of adenoviruses and enterovirusesSamples collected throughout the ultracentrifugationprocess were pre-treated with 1× RNAsecure (Life Tech-nologies, Carlsbad, CA) and 5 units (U) of DNase I (Epi-centre Biotechnologies, Madison, WI). This reaction wasterminated by adding 10 mM EDTA (pH 8.0) for 15 minat 65 °C. DNA and RNA from adenoviruses and entero-viruses, respectively, from were extracted using theNucliSens easyMAG system (bioMérieux, Craponne,France). Nucleic acids were further precipitated using0.1 volumes of 3-M sodium acetate and two volumes of100 % ethanol, washed with 1 mL of ice-cold 70 % etha-nol, and resuspended in 10 mM Tris solution. Nucleicacid concentration and purity was assessed with QubitdsDNA high sensitivity and RNA assay kits in a Qubit2.0 fluorometer (Life Technologies, Carlsbad, CA) andNanoDrop spectrophotometer (NanoDrop technologies,Inc., Wilmington, DE), respectively.Quantitative polymerase chain reaction (qPCR) ofadenoviruses and enterovirusesQuantitation of adenovirusesDetection of adenoviruses was carried out using a com-bination of primers described by Wong et al., 2008 [35](Table S1). These primer sets amplify a conserved region(81–87 bp) of the hAdV hexon gene. DNA extractedfrom raw samples was used as template to generateamplicons for standard curve. PCR conditions were con-ducted as follows: 94 °C for 5 min, followed by 35 cyclesof 94 °C for 30 s, 53 °C for 30s, 72 °C for 30 min, and afinal extension at 72 °C for 10 min. PCR amplicons werepurified with a QIAQuick PCR Purification Kit (QiagenSciences, Maryland, MD) according to the manufac-turer’s instructions.Quantitation of enterovirusesRNA (4 μl) extracted from raw samples was first treatedwith Turbo DNase I (Life Technologies, Carlsbad, CA)following the manufacturer’s instructions. RNA was thenconverted into complementary DNA (cDNA) usingSuperscript III reverse transcriptase (Life Technologies,Carlsbad, CA). Amplification of the UTRe gene in en-teroviruses was conducted using primers described byVerstrepen et al. [36] and Watzinger et al. [37] (TableS1). This primer set amplifies a specific 148-bp regionwithin this gene. cDNA from raw samples was used astemplate to generate amplicons for standard curve. PCRconditions were conducted as follows: 94 °C for 5 min,followed by 35 cycles of 94 °C for 30 s, 51 °C for 30s,72 °C for 30 min, and a final extension at 72 °C for10 min. PCR amplicons were purified with a QIAQuickPCR Purification Kit (Qiagen Sciences, Maryland, MD)according to the manufacturer’s instructions.Standard curves for adenoviruses and enteroviruseswere generated by ligating purified amplicons of adeno-viruses and enteroviruses into pCR2.1-TOPO cloningvectors (Invitrogen) and transformed into One Shot E.coli DH5α-T1R competent cells following the manufac-turer’s protocol. One transformant was selected andgrown overnight at 37 °C in LB broth with 50 μg/mL ofkanamycin. Plasmids were extracted and purified usingPurelink Quick Plasmid Miniprep kit (Life Technologies,Carlsbad, CA) and quantified using Qubit dsDNA highsensitivity assay kit (Life Technologies, Carlsbad, CA).Plasmid DNA was linearized by digestion with theBamHI-HF endonuclease (New England BioLabs Inc.,Ipswich, MA). Serial dilutions of the linearized plasmidwere used as templates to generate standard curves forqPCR and RT-qPCR. Each 20-μl real-time PCR mixtureconsisted of 10 μl of Fast SYBR Green Master Mix (2X)Real-Time PCR Master Mix (Life Technologies, Carlsbad,CA), 250 nM each primer, and 1 μl of template DNA orcDNA. The thermal cycling conditions consisted of initialdenaturation for 20 s at 95 °C, followed by 40 cycles of 3 sat 95 °C and 20 s at 60 °C. Gene copy numbers for eachsample were run in triplicate using a 7900 HT Fast Real-Time PCR system (Life Technologies, Carlsbad, CA). Toverify the absence of non-specific amplification, a dissoci-ation step was included and amplicons were analyzed on a1.5 % agarose gel.Nucleic acid extraction and quality controlsBefore extraction and to facilitate disruption ofeukaryotic cells, eight freeze-thaw cycles, followed byovernight proteinase K digestion (Qiagen Sciences,Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 4 of 19Germantown, MD), were conducted for this fraction[38]. DNA was extracted from eukaryotes and bacterialcell fractions using the UltraClean Soil DNA Kit (MoBio,Carlsbad, CA) as per the manufacturer’s instructions.Concentrated viral-sized particles were pre-treated with1X RNAsecure (Life Technologies, Carlsbad, CA) and5 U of DNase I (Epicentre Biotechnologies, Madison,WI). This reaction was terminated with 10 mM EDTA(pH 8.0) for 15 min at 65 °C. Total nucleic acids wereextracted from the viral fraction using the NucliSenseasyMAG system (bioMérieux, Craponne, France). Nu-cleic acids from all fractions were further precipitatedusing 0.1 volumes of 3-M sodium acetate, two volumesof 100 % ethanol, and 5 μl of 5 μg/μl linear acrylamide.Samples were stored at −80 °C overnight then centri-fuged at 17,000×g for 30 min at 4 °C. Supernatants werediscarded, and pellets were washed with 70 % ice-coldethanol, air dried, and resuspended in 10 mM Tris Cl,pH 8.5. Concentration, purity, and average size of nu-cleic acids were assessed with Qubit dsDNA High Sensi-tivity or RNA Assay kits in a Qubit 2.0 fluorometer (LifeTechnologies, Carlsbad, CA), NanoDrop spectropho-tometer (NanoDrop Technologies, Inc., Wilmington,DE), and Agilent High Sensitivity DNA kit (AgilentTechnologies, Inc., Santa Clara, CA), respectively.Cysts and oocysts from Giardia lamblia and Crypto-sporidium parvum (Waterborne, Inc., New Orleans,LA), respectively, were used as positive control for DNAextraction and amplification of the 18S rRNA gene. Anisolate of Aspergillus flavus was used as a control foramplification of the ITS region. A strain of E. coli(ATCC 25922) was used as positive control for 16SrRNA and cpn60 genes. For DNA viruses and g23 gene,a myovirus propagated in Synechococcus sp. strainWH7803 was used as a positive control. As a positivecontrol for RNA viruses and RdRp amplicons, culturesof Heterosigma akashiwo were grown and infected withHaRNAV (isolate SOG263). Negative controls includedsterile water and PBS.cDNA synthesis and random amplification of the viralfractionA modified adapter nonamer approach described byWang et al. [39] was used for cDNA synthesis and in-crease yields of the viral fraction. An aliquot of 4 μl fromthe total nucleic acids in the viral fraction was treatedwith Turbo DNase I (Life Technologies, Carlsbad, CA),following the manufacturer’s instructions. DNAsed sam-ples (RNA) were then converted to cDNA using randomnonamer primer A (5′-GTTTCCCACTGGAGGATA-N9-3′) and Superscript III reverse transcriptase (LifeTechnologies, Carlsbad, CA). Second strand synthesiswas carried out using two rounds of Sequenase Version2.0 DNA Polymerase (Affymetrix, Santa Clara, CA).Samples were stored at −20 °C until further processing.Subsequently, 5 μl of double-stranded cDNA sampleswas used as templates in a 50-μl PCR reaction consistingof 5 U of KlenTaq LA polymerase, 1X Klentaq PCRbuffer, 0.2-mM nucleotides, 2 μM of primer B (5′-GTTTCCCACTGGAGGATA-3′). Random amplificationwas carried out as follows: 94 °C for 4 min, 68 °C for5 min followed by 30 cycles of 94 °C for 30 s, 50 °C for1 min, and 68 °C for 1 min and a final extension at 68 °Cfor 2 min. The amplified material was then cleaned upwith Agencourt AMPure XP-PCR purification system(Beckman Coulter Inc., Brea, CA) at a 1.8× ratio. Primer Bwas excised using 4 U of BpmI (New England BioLabsInc., Ipswich, MA). Digested products were cleaned upwith Agencourt AMPure XP-PCR purification system(Beckman Coulter Inc., Brea, CA) at a 1.8× ratio. Finally,samples were end-repaired using 0.2-mM nucleotides, 1×T4 ligase buffer, 3 U of T4 DNA polymerase, 5 U of DNApolymerase I large (Klenow) fragment, and 10 U of T4polynucleotide kinase (New England BioLabs Inc., Ips-wich, MA). For random amplification of viral DNA, therandom nonamer primer A and Sequenase DNA Poly-merase were used as described above. Fragments gener-ated in the random amplification process were furtheranalyzed using the Agilent High Sensitivity DNA Kit(Agilent Technologies, Inc., Santa Clara, CA) and quan-tified using the Qubit dsDNA High Sensitivity Assay Kit(Life Technologies, Carlsbad, CA).Amplification of gene targetsTable 2 summarizes the primer sets and conditions usedfor the generation of amplicons described in the presentstudy. Nucleic acids extracted from water samples andcontrols were analyzed for V1–V3 regions of the 18SrRNA gene and internal transcribed spacer (ITS1/ITS2) region for eukaryotes; hypervariable V3–V4 re-gions of the 16S rRNA and cpn60 genes for bacteria;and g23 for T4-like bacteriophages and the RdRp genefor picorna-like viruses. Each PCR reaction consistedof 1.5-mM MgCl2, 0.2-mM nucleotides, 0.4 μM ofprimers, 1.25 U of Hot Start Polymerase (Promega Cor-poration, Fitchburg, WI), 1:10 dilution of templateDNA, and water in a 50-μl volume. Fragments of thecpn60 gene were amplified using a primer mixture con-taining a 1:3 M ratio of primers H279/H280 andprimers H1612/H1613 as described by Schellenberg etal. [46]. RNA-dependent RNA polymerase genes wereamplified using Illustra Ready-To-Go PCR Beads (GEHealthcare UK Limited, Buckinghamshire, UK), 0.4 μMof primers, 1 μl of randomly amplified viral cDNA, andwater in a 25-μl volume. PCR amplicons were run induplicates, examined in a 1.5 % agarose/0.5X TBE gelstained with 1X GelRed (Biotium, Inc., Hayward, CA),and purified with a QIAQuick PCR Purification KitUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 5 of 19(Qiagen Sciences, Maryland, MD) according to themanufacturer’s instructions.Quantitative polymerase chain reaction of eukaryotes,bacteria, E. coli, and T4-type bacteriophagesEstimates of eukaryotes, bacteria, E. coli, and T4-typebacteriophage quantities in watershed sites were deter-mined using β-tubulin, 16S rRNA, uidA, and g23 genefragments, respectively (Table 2). Gene copy numberswere calculated as previously described by Ritalahti et al.[48]. A modification based on sample dilution and vol-ume was introduced to this calculation in terms ofGCNs per milliliter sample. Standard curves for qPCRwere generated using serial dilutions of linearizedpCR2.1-TOPO vector (Life Technologies, Carlsbad, CA)with either cloned β-tubulin, 16S rRNA, and g23 genes.E. coli genomic DNA was used for standard curves foruidA gene. Each 20-μl real-time PCR mixture consistedof 10 μl of Fast SYBR Green Master Mix (2X) Real-TimePCR Master Mix, 250 nM of each primer, and 1 μl oftemplate DNA. Quantitation of the uidA gene fragmentused Taqman Universal PCR Master Mix (Life Tech-nologies, Carlsbad, CA) and followed the conditions,oligonucleotides (400 nM), and probe (200 nM) concen-trations described by Maheux et al [49]. SYBR green-labeled reactions were conducted on a 7900 HT FastReal-Time PCR system (Life Technologies, Carlsbad,CA), while Taqman-labeled reactions were carried outon a 7500 Fast Real-Time PCR system (Life Technolo-gies, Carlsbad, CA). Each qPCR was run in triplicate. Toverify the absence of non-specific amplification, a dis-sociation step was included in the SYBR green-labeledreactions, and amplicons were visualized on a 1.5 %agarose gel.DNA library preparation and sequencingLibraries of 18S rRNA, ITS, 16S rRNA, g23, and RdRpamplicons were prepared using the NEXTflex ChIP-SeqKit (BIOO Scientific, Austin, TX) with the gel-sizeselection option provided in the manufacturer’s in-structions. The universal target region of the cpn60gene was amplified using a 1:3 primer cocktail ofH279/H280:H1612/H1613 as previously described bySchellenberg et al. [46].Bacterial genomic DNA libraries were prepared usingthe Nextera XT DNA sample preparation kit (Illumina,Inc., San Diego, CA). One nanogram of bacterial DNAwas fragmented following the manufacturer’s instructions.Libraries from randomly amplified viral DNA and cDNAfractions were prepared using NEXTflex ChIP-Seq kit(BIOO Scientific, Austin, TX) by following a gel-free op-tion provided in the manufacturer’s instructions.Amplicon, bacterial, and viral library sequencing wereperformed on an Illumina MiSeq (Illumina, Inc., SanDiego, CA) using MiSeq reagent kits V2 with 150- and250-bp paired-end outputs. cpn60 pyrosequencingTable 2 Description of primers used in PCR and quantitative PCRTargetgenePrimer name and sequences (5′ ➔ 3′) Ampliconsize (bp)Thermal program References18S rRNA EuK1A: CTGGTTGATCCTGCCAG499R: CACCAGACTTGCCCTCYAAT~500 94 °C × 5 min, 35 cycles of 30 s at 94 °C, 60 s at 55 °C,and 90 s at 72 °C, and a final cycle of 10 min at 72 °C.[40, 41]ITS ITS1: TCCGTAGGTGAACCTGCGGITS4: TCCTCCGCTTATTGATATGC~500 95 °C × 15 min, 35 cycles of 30 s at 95 °C, 30 s at 55 °C,and 90 s at 72 °C, and a final cycle of 10 min at 72 °C.[42]β-tubulin(qPCR)BT107F: AACAACTGGGCIAAGGTYACTACACBT261R: ATGAAGAAGTGGAGICGIGGGAA~450 Initial denaturation 20 s at 95 °C, followed by 40 cyclesof 1 s at 95 °C and 30 s at 60 °C.[43]16S rRNA 341F: CCTACGGGAGGCAGCAGR806: GGACTACHVGGGTWTCTAAT~465 94 °C × 5 min, 35 cycles of 45 s at 94 °C, 45 s at 50 °C,and 60 s at 72 °C, and a final cycle of 10 min at 72 °C.[44, 45]cpn60 H279: GAIIIIGCIGGIGAYGGIACIACIACH280: YKIYKITCICCRAAICCIGGIGCYTTH1612: GAIIIIGCIGGYGACGGYACSACSACH1613: CGRCGRTCRCCGAAGCCSGGIGCCTT~578 3 min at 94 °C, 40 cycles of 30 s at 94 °C, followed by atemperature gradient of 1 min at 42 °C, 48 °C, 54 °C, or60 °C, and 1 min at 72 °C, followed by a final extensionof 10 min at 72 °C.[46]16S rRNA(qPCR)341F: CCTACGGGAGGCAGCAG518R: ATTACCGCGGCTGCTGG~194 Incubation 2 min at 50 °C. Initial denaturation 20 s at95 °C, followed by 40 cycles of 1 s at 95 °C and 20 sat 60 °C.[44]uidA(qPCR)784F: GTGTGATATCTACCCGCTTCGC866R: GAGAACGGTTTGTGGTTAATCAGGAEC807: FAM-TCGGCATCCGGTCAGTGGCAGT-BHQ184 Incubation 2 min at 50 °C. Initial denaturation 10 minat 95 °C, followed by 40 cycles of 15 s at 95 °Cand 1 min at 60 °C.[47]g23(qPCR)MZIA1bis: GATATTTGIGGIGTTCAGCCIATGAMZIA6: CGCGGTTGATTTCCAGCATGATTTC~471 94 °C × 1.5 min, 35 cycles of 45 sat 94 °C, 60 s at 50 °C, and 60 sat 72 °C, and a final cycle of5 min at 72 °C.Incubation 2 min at 50 °C.Initial denaturation for 20 sat 95 °C, 40 cycles of 1 s at95 °C and 30 s at 60 °C.[20]RdRp RdRp1: GGRGAYTACASCIRWTTTGATRdRp2: MACCCAACKMCKCTTSARRAA~450 94 °C × 75 s, 40 cycles of 45 s at 94 °C, 45 s at 50 °C, and 60 sat 72 °C, and a final cycle of 5 min at 72 °C.[26]Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 6 of 19libraries were sequenced on a Roche 454 GenomeSequencer FLX Titanium following standard protocols(Laboratory for Advanced Genome Analysis, VancouverProstate Centre). Additionally, PhiX sensu lato, an adapter-ligated ssDNA virus was used as control in Illumina se-quencing. Amplicon libraries used 5 % PhiX, while that forbacterial and viral metagenome libraries used 1 % PhiX.Amplicon and metagenomic sequencing control gen-omic DNA from four bacterial strains was used as 16SrRNA gene amplicon and metagenomic sequencing con-trol. Bacterial mock community included Nocardioidessp. JS614, Pseudomonas aeruginosa PA01, Rhodobactercapsulatus SB1003, and Streptomyces coelicolor A3. Viralmock community consisting of genomic DNA andcDNA from myovirus and HaRNAV as well as g23 andRdRp amplicons was used as sequencing controls. Bac-terial and viral mock communities were pooled in equalmolar concentrations, indexed, and sequenced with theenvironmental samples described in this study. Sequen-cing controls were not included for the eukaryotic frac-tion (18S rRNA and ITS).Data analysisGene copy number (GCN) or flow cytometry count(FCM) data were log10 transformed for analysis. One-way analysis of variance was run using Statistical Ana-lysis System (SAS, version 9.1.3 for Windows) on theqPCR and FCM data to detect differences among targetmicrobial fractions. Tukey’s test was used to determinestatistical differences among the different sites. Correla-tions were assessed using Spearman correlation coeffi-cients. A p value of 0.05 was assumed for the test as aminimum level of significance.Adapter and primer sequences of amplicon and virallibraries were removed using Cutadapt [50], while short(<100 bp)- and low-quality reads were discarded usingTrimmomatic version 0.32 [51]. Forward reads of ampli-con and viral libraries were uploaded to the Metage-nomic Rapid Annotations using Subsystems Technology(MG-RAST) [52] and Metavir [53], respectively. Bacter-ial amplicon analysis was also performed using QIIME[54] to identify trends robust to analysis platform. Theraw data from cpn60 amplicon sequencing was proc-essed through microbial profiling using metagenomic as-sembly (mPUMA) pipeline [55]. Bacterial metagenomesequence reads were trimmed using Adapter and Adap-terRead2 parameters embedded in the MiSeq Reportersoftware (Illumina, Inc., San Diego, CA). Furthermore,paired-end sequences were merged using PANDAseq[56] and then uploaded to the MG-RAST pipeline [53].Short (<151 bp) and unmerged bacterial metagenomicreads were discarded.Taxonomic classifications for eukaryotic and bacterialamplicon and bacteria metagenomic sequence readswere based on the lowest common ancestor method[57]. The MG-RAST bacterial metagenomic results weresubsequently confirmed by analysis with MEGAN4 [58].For viral reads, taxonomic composition was computedusing BLASTx from the NCBI website and adjusted vialength normalization using the Genome relative Abun-dance and Average Size (GAAS) Metagenomic Tool[59]. Functional gene composition for bacterial and viralmetagenomes was annotated using MG-RAST and theSEED subsystems [60]. A minimum percent identity of60 % and annotations with an e value cutoff of 10-3 orless were used for further analyses. Microbial diversityand richness indexes were calculated using EstimateS(version 9.1.0) [61], available from http://viceroy.eeb.u-conn.edu/estimates/. Multivariate analysis was per-formed for bacterial and viral metagenomes andamplicons using the Bray-Curtis metric.Results and discussionApproximately 40 L of raw water was collected fromwatershed sites in BC during a 1.5-month period (Spring2012). A combination of conventional and tangentialflow filtration was used to separate eukaryotic-, bacter-ial-, and viral-sized particles, followed by nucleic acid ex-traction for these microbial fractions. The utility of theprotocol was tested in terms of the quality of the result-ing sequence libraries and the ability to characterize themicrobial communities. Additional file 1: Table S2 sum-marizes the water quality parameters measured at eachwatershed location.Efficiency of filtration of microbial communitiesDead end and tangential flow filtration (TFF) havewidely been used for the separation of microbial com-munities in water [26, 32, 62]. A significant correlation(96.1 %, p ≤ 0.0007) was observed between viral-like par-ticles and bacterial cell counts by flow cytometry (Add-itional file 1: Table S3). Flow cytometry counts in rawwater detected between 5.03 × 106 and 1.18 × 108 virus-like particles per milliliter of sample, while bacterialcounts ranged between 1.55 × 105 and 1.24 × 106 cells/mL of environmental water. Virus-like particles were sig-nificantly higher (p < 0.0001) in APL compared to otherwatershed locations. Bacterial cell counts in APL werehigher compared to watershed locations (p < 0.0001), ex-cept ADS (p = 0.4231). Overall, TFF was able to achievea 94-fold concentration of the viral fraction from an ini-tial volume of ~38.7 L to an average final volume of415 mL. Viral concentration efficiency averaged 6 ± 51 %while bacterial concentration efficiency averaged 90 ±11 %. The wide range in viral recovery efficiencies maybe associated with losses during filtration [63–65].Water with high turbidity and suspended solids tend tosaturate filters [66, 67], and lower recovery efficienciesUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 7 of 19were observed in agricultural samples (APL and ADS),where turbidity and total dissolved solids values werehigher (Additional file 1: Table S2).Ultracentrifugation as a method to improve recovery ofvirusesAssessment of ultracentrifugation to further concentrateviral particles was performed using qPCR and FCM foradenoviruses and enteroviruses spiked in different vol-umes of MEM. Comparable recovery efficiencies havebeen reported with ultracentrifugation [68, 69]. Additionalfile 1: Figure S1 depicts quantitation of adenovirus (A) andenterovirus (B) per milliliter sample throughout differentstages of the ultracentrifugation process and over time (1,2, and 4 h). A gradual decrease in terms of viral GCNsand particles per milliliter was observed in supernatantscollected at different time points of 1, 2, and 4 h usingboth approaches.Recovery efficiency as measured by qPCR was esti-mated to be 54.2 and 68.2 % for adenoviruses and en-teroviruses, respectively. Recovery efficiencies were alsodetermined by flow cytometry with average percentagesof 160 ± 26.3 % for adenoviruses and 0.5 ± 0.1 % for en-teroviruses. Correlation analysis between qPCR ap-proach and flow cytometry counts detected coefficientsof 0.9206 (p = 0.0004) and 0.8683 (p = 0.0024) for adeno-viruses and enteroviruses, respectively (Additional file 1:Figure S2). The observed differences between qPCR andFCM to quantify virus particles for enteroviruses may beassociated to FCM underestimating ssRNA viruses<30 nm in diameter [70, 71]. In this work, we used Cox-sackie B2 enterovirus, which is approximately 30 nm.This may indicate that only a fraction of this enteroviruswas measurable by FCM as compared to the qPCR ap-proach. It is also possible that some of these cells con-taining viruses may have been caught in the 0.2-μmfilters. This extra step was conducted to filter out celldebris as well as simulate the filtration system used inthis research. Although qPCR approach seemed to bemore sensitive to detect adenoviruses and enterovirusesin this validation experiment, the lack of a highly con-served viral gene makes the quantitation of viruses diffi-cult compared to other microbial fractions such asbacteria or eukaryotes. Thus, FCM was the methodchosen to monitor viral-like particles in water samples.In this study, recovery rates using ultracentrifugation toconcentrate virus-like particles in watershed samples andquantified using FCM were between 52.9 and 114.8 %(urban sites, data not shown).Nucleic acid yields and quality assessmentAlthough the nucleic acid yields from this study werecompared to other similar studies, direct comparisons aredifficult given the differences in water matrix conditionsand procedures used (Table S4). Overall nucleic acid yields(excluding viral RNA fraction) had the same order of mag-nitude across the different filter pore sizes used in thisstudy (Table S4). Total RNA extracted from the viral-sizedfraction could only be detected in agricultural sites.Nucleic acid purity was also estimated (Table S4). TheA260/A280 and A260/A230 ratios (that indicate potentialprotein and humic acid contamination) were >1.4 andbetween 0.5 and 2.1, respectively. Similar results havebeen reported for A260/A280 and A260/A230 ratios usingcommercial kits and automated platforms for nucleicacid extraction from environmental samples [72–76].While the A260/A230 ratio suggested humic acid contam-ination, it did not inhibit downstream applications suchas PCR, qPCR, random amplification, library prepar-ation, and sequencing.Amplification and quantitation of microbial fractionsPolymerase chain reactionThe utility of the protocol was tested using a PCR-basedtargeted sequencing approach for all three fractions.While 18S rRNA and ITS (eukaryotes), 16S rRNA andcpn60 (bacteria), and g23 (T4-type bacteriophage) weredetected in all watershed sites, RdRp amplicons (picorna-like viruses) could only be detected in agricultural sites(AUP, APL, and ADS) and the urban downstream site(UDS). Picorna-like viruses have been reported in BritishColumbia waters and mainly coastal waters infectingeukaryotic phytoplankton [28, 77]. In this study, RdRpfragments were found in watershed sites where dissolvedsolids and turbidity values were higher compared to othersites (Additional file 1: Table S2). Moreover, in experimen-tal observations, RdRp fragments have been detected con-sistently over time in agricultural sites where conductivityand derived parameters such as salinity, specific conduct-ance, and total dissolved solids are relatively higher (datanot shown). The detection of these picorna-like viruses ina freshwater environment may also be attributable to ter-restrial runoff or excretion by birds and fish [11, 78].Random amplificationViral RNA yields were lower compared to the viralDNA, eukaryotic, and bacterial fractions (Table 2). Al-though viruses are the most abundant entities in the en-vironment, viruses only make up ~5 % of the relativebiomass within microbial communities [79]. The smallquantities of viral nucleic acids represent a challenge fordownstream applications. Large volumes (from tens tohundreds of liters) of water are typically required to iso-late and concentrate viral nucleic acids [11, 27, 80, 81].The average fragment lengths of the amplified viralcDNA and DNA ranged from 200 to 2 kb with an aver-age length of 400 bp (data not shown), which is similarto other viral studies [82, 83].Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 8 of 19Quantitation of microbial fractionsQuantitative PCR and FCM are powerful culture-inde-pendent methods used to quantitate microbial frac-tions or organisms in a variety of environments.Limitations exist for both approaches in terms of reso-lution, technical difficulty, variance, and dynamicrange [84]. Microbial eukaryotes captured by the filtra-tion system ranged between 1 to 105 μm in size, but inthis study, a size cutoff of 5 μm was used for the largerorganisms, suggesting that a significant portion of themicrobial eukaryotes would have not been detected bythe FCM. Another major constraint of FCM is the dif-ficulty in designing a compatible dye or target-specificantigen for a specific target such as E.coli or T4-likemyoviruses. In contrast, qPCR targeting specific genesare much simpler to design and implement. In thisstudy, copy numbers of β-tubulin, 16S rRNA, uidA,and g23 genes were estimated using qPCR (Fig. 1). Dueto inaccuracies of DNA measurement by spectrophoto-metric methods, especially in the presence of inhibitorsand contaminants, the GCNs reported in this studyrely upon fluorometric measurements using the Qubitinstrument (Table S4). As β-tubulin and 16S rRNAgenes are multicopy genes, average factors of 1.93 (β-tubulin for eukaryotes) [85, 86] and 4.3 (16S rRNAgenes for bacteria) [87] were used to normalize GCNsper nanogram and milliliter sample. The uidA gene is asingle copy gene that encodes ß-D-glucuronidase in E.coli [88]. Quantitation of major capsid gene fragmentsfor T4-like bacteriophages (g23) was conducted usingviral DNA template with no random amplification step.Although the primer sets used to quantify GCNs werespecific for these microbial fractions (Table 3), andnon-specific amplification was not detected, PCR efficiencywas low (~54 %) for β-tubulin and g23. This efficiency mayhave been improved by targeting a smaller DNA fragment(<300 bp); however, amplification of a shorter fragmentof β-tubulin [86] was not successful in our samples, andthe hypervariable regions within g23 preclude qPCR of ashorter fragment [20, 89].Fig. 1 Gene copy numbers of 16S rRNA (a), uidA (b), β-tubulin (c), and g23 (d) gene fragments detected in watershed sites. UPL urban polluted,UDS urban downstream, AUP agricultural upstream site, APL agricultural polluted, ADS agricultural downstream, PUP protected upstream, PDSprotected downstream. Black bars represent the mean GCN normalized per nanogram of DNA in each location (n = 3). Gray bars represent themean GCN normalized per milliliter of sample (n = 3). Error bars indicate standard deviations. Means with different letters indicate statisticalsignificance between watershed sites at the 0.05 levelUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 9 of 19Estimates of 16S rRNA gene abundances (Fig. 1a) weresimilar to those detected in other aquatic environments[86, 90–93]. The concentrations of prokaryotic cells esti-mated using 16S rRNA gene copies and flow cytometrycounts were not significantly correlated (p > 0.05). GCNsof the 16S rRNA gene per milliliter of sample were be-tween 0.8 to 1.6 orders of magnitude higher comparedto FCM counts. Overestimation of prokaryotes by 16SrRNA qPCR can be associated with the multicopy natureand intragenomic heterogeneity of 16S rRNA [94, 95].Quantitation of E. coli using the uidA gene (Fig. 1b) in-dicated that E. coli represented only 0.074 and 0.025 %of the biomass (GCN/ng DNA) and volume (GCN/mLof sample), respectively, within the bacterial fraction.Estimates of eukaryote abundance using the β-tubulingene indicated a range between 102 to 104 organismsacross the watershed sites studied (Fig. 1c), which iscomparable to previous studies [86, 96–98]. Quantita-tion of g23 estimated the presence of between 102 and104 T4-like bacteriophages per nanogram of DNA andper milliliter of sample (Fig. 1d). As the g23 gene isfound in T4-type bacteriophages, these numbers repre-sent only a small fraction of the entire viral communitythat infects bacteria and an even smaller proportion ofthe entire viral community. While a comparison betweeng23 and other viral groups is difficult, quantitation re-sults via qPCR for other DNA viral groups such asadenovirus and JC polyomavirus in other freshwater eco-systems [99] were within the same order of magnitudeas our samples.Variables such as total coliform and E. coli counts,specific conductivity, total dissolved solids, salinity, tur-bidity, dissolved chloride, ammonia, orthophosphates,nitrites, and nitrates were found to be significantly cor-related with g23 GCNs/mL (p ≤ 0.0280, data not shown).As these variables increased, the abundance of majorcapsid genes increased as well. This finding suggests thatthese environmental variables and enterobacteria mayhave influence on the viral population, particularly T4-like myoviruses as previously reported in other fresh-water ecosystems [100]. No other significant correlationswere detected between the other two microbial fractionsand environmental parameters.The ratios of GCNs between β-tubulin, 16S rRNA,uidA, and g23 were also determined. A ratio of 1:103was determined for comparison between E. coli (uidA)and total bacteria (16S rRNA gene). A further compari-son between total E. coli counts (uidA gene fragments)and culturable E. coli cells (Colilert) indicated a differ-ence of two to three orders of magnitude higher forquantitation using the uidA gene. This variation betweenculture-based and molecular-based E. coli assays hasbeen previously reported [101]. The ratio of both β-tubulinand g23 GCNs to 16S rRNA GCNs was on average 1:100,similar to other aquatic ecosystems [86, 97, 102–104]. Asecological relationships in aquatic environments are com-plex, the ratios described here only represent early insightsinto the microbial community interactions of these water-shed locations.Microbial community structure in watershedsAlthough a small number of samples were analyzedusing the Bray-Curtis metric, the protected downstream(PDS) site stood apart from all sites (Additional file 1:Figure S5). Biofilms present in the 8.8-km pipe (Table 2)may have affected the microbial community compositionresulting in a distinctive pattern for PDS compared toother watershed locations. The microbial communitiesnot impacted by urban or agricultural activities, suchas PUP and AUP, were more similar to one another(Additional file 1: Figure S5). Additional file 1: TablesS5 and S6 summarize read lengths and CG-contents ofamplicon and metagenomic libraries, respectively.Most of the rarefaction curves in the metagenomicand amplicon libraries plateaued (with singleton se-quences removed), suggesting that most of the diversitywithin the eukaryotic, bacterial, and viral communitieswas captured. Diversity and richness indices were alsocalculated (Additional file 1: Tables S7 and S8). Althoughrarefaction curves approached an asymptote, there weredifferences in terms of community structure in each tar-get fraction across the watershed sites. For instance,APL had the greatest diversity and richness values forbacteria based on the metagenomic data (Additional file1: Figure S4 and Table S8). However, this communitypattern changed when 16S rRNA and cpn60 ampliconswere used (Additional file 1: Figure S3 and Table S7).These differences reflect the biases in PCR amplification,multicopy gene abundance, variation in genome sizes, li-brary preparation, and normalization methods [105–107].Thus, comparisons can only be made between samplesprepared and analyzed using the same methods. In thepresent study, our main goal was to demonstrate theTable 3 Relative abundance (%) of E. coli in watershed sitesusing amplicon and metagenome approachesWatershed site 16S rRNA* cpn60* Bacterial metagenome*UPL 0.71 (198854) 0.24 (5955) 0.19 (44463)UDS 0.65 (205568) 0.15 (10674) 0.17 (70203)AUP 3.95 (253363) 2.62 (26641) 1.92 (48059)APL 0.94 (38376) 1.54 (43417) 1.43 (169295)ADS 0.34 (86499) 0.43 (53794) 0.44 (29399)PUP 1.69 (66825) 0.60 (8947) 0.06 (71837)PDS 0.16 (320422) 0.02 (11374) 0.42 (68525)Numbers in parentheses represent total number of reads post quality filtering*Correlation coefficients: 16S rRNA and cpn60 (p value = 0.0104, rs = 0.8726);cpn60 and bacterial metagenome (p value = 0.0018, rs = 0.9374)Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 10 of 19utility of a size fractionation method to capture most ofthe diversity and richness of the microbial communitypresent in these watersheds. Percentage of significant hitsassigned to each amplicon and bacterial and viral meta-genomes are listed in Additional file 1: Table S9 and fur-ther detailed in Additional file 2.Characterization of microbial eukaryotic communities inwatersheds using 18S rRNA and ITS ampliconsMajor taxa identified by 18S rRNA sequencing includedChlorophyta, Arthropoda, Streptophyta, Chytridiomycota,Apicomplexa, Nematoda, and Chordata (Fig. 2) while in-ternal transcribed spacer (ITS) sequencing identifiedmajor groups such as algae, Chlorophyta, and two fungalphyla, Basidiomcycota and Ascomycota. Streptophytawere detected in the same abundance (16 %) with both18S rRNA and ITS targets across the sites (Fig. 2). Othertaxa detected by ITS sequencing had a lower representa-tion than the same groups measured by 18S rRNA.Approximately 20 % of the 18S rRNA sequences wereassigned as unclassified groups within Eukaryota, with lessthan 1 % related to fungi. In contrast, ~4.9 % of the ITSsequences were unclassified eukaryotic groups with 50 %of the taxa associated to unclassified fungi. Biases in amp-lification and sequencing (including sequencing platform)are well described for the 18S rRNA and ITS targets[108–111] and for other microbial targets as well [112].Overall, Chlorophyta were a dominant group using both18S rRNA and ITS sequencing in the agricultural im-pacted sites (APL and ADS), while Streptophyta appearedmore abundant in urban sites with the 18S rRNA sequen-cing approach. Ascomycota were observed to be dominantin urban sites (UPL and UDS) with the ITS approachcompared to other watershed locations. In this study, both18S rRNA and ITS sequencing indicated differencesamong eukaryotic groups within each watershed.Characterization of bacterial communities in watershedsAbundant phyla of bacteria identified in the watershedsamples included the four predominant phyla in theFig. 2 Relative abundance of eukaryotic (18S rRNA and ITS), bacterial (16S rRNA and cpn60), and viral communities (g23 and RdRp) identified inwatershed sites. UPL urban polluted, UDS urban downstream, AUP agricultural upstream site, APL agricultural polluted, ADS agriculturaldownstream, PUP protected upstream, PDS protected downstreamUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 11 of 19human and wildlife gut and in sediments and soil[113–115]: Proteobacteria (Alpha-, Beta-, Gamma-, Delta-,and Epsilonproteobacteria), Actinobacteria, Firmicutes,and Bacteroidetes (Fig. 3). Betaproteobacteria was themost abundant class of the Proteobacteria and made up~17, 35, and 11 % of the bacterial community in ampliconand metagenome libraries from urban, agricultural im-pacted, and protected watersheds, respectively. While thisclass is a common feature reported in freshwater environ-ments [116], significant differences (p ≤ 0.0184) were de-tected between agricultural and urban sites and protectedwatersheds.Of the Firmicutes, order Clostridiales made up 73 % inagricultural impacted watersheds, 68 % in urban im-pacted watersheds, and 53 % in protected watersheds.Across watersheds, approximately 60 % of Clostridialesbelonged to Clostridiaceae family. Other familiesaccounted for ~30 % of the Clostridiales and includedRuminococcaceae, Peptococcaceae, Eubacteriaceae, Lach-nospiraceae, and Syntrophomonadaceae. In small percent-ages (≤3 %), Heliobacteriaceae, Peptostreptococcaceae,and Clostridiales families XI, XVII, and XVIII were also ob-served. Interestingly, in agricultural- and urban-impactedwatersheds, families such Ruminococcaceae and Lachnos-piraceae were observed to be at least onefold higher than inprotected sites (~1.5 %), while members of Peptostrepto-coccaceae were completely absent from protected water-sheds. Genera of these families have been reportedassociated to fecal and sewage infrastructures [12, 117].Another major order in Firmicutes was Bacillales; esti-mates of 13, 16, and 32 % were observed in agricultural,urban, and protected watersheds, respectively. The major-ity of Bacillales belonged to Bacillaceae (~60 %) andPaenibacillaceae (~20 %). This abundance of Bacillales infreshwaters is supported by the oxygenated and non-impacted nature of protected watersheds compared toagricultural or urban watersheds [118].Both amplicon and metagenomic sequencing also re-vealed that Actinobacteria had high abundance in pro-tected watersheds (~20.3 %), particularly PDS (~32 %)compared to urban (4.5 %) and agricultural (7.0 %) influ-enced watersheds (Figs. 2 and 3). Although Actinobac-teria has routinely been reported as soil bacteria, itsoccurrence in freshwater and mainly in pristine waterbodies could be attributed to soil pH [119, 120]. Withinthis phyla, order Actinomycetales made up >84 % ofActinobacteria in all watersheds. On average, 16 % ofActinomycetales pertained to Streptomycetaceae inurban and protected watershed, while in agriculturalsites, this family represented 5 % or less. A high abun-dance of bacterial groups within Streptomycetaceae hasbeen associated to the production of geosmin and 2-methylisoborneol, which impact water quality [121, 122].Other orders included Solirubrobacterales, Rubrobacter-ales, Acidimicrobiales, Coriobacteriales, and Bifidobacter-iales, which made up less than 10 % of the Actinobacterialcommunity in watersheds. Within order Bifidobacteriales,Bifidobacteriaceae (ubiquitous inhabitants of the gastro-intestinal tract of mammals) were dominant in urban im-pacted sites (~3.4 %) compared to agricultural (~1.2 %) orprotected (~0.4 %) watersheds.Although only seven environmental samples were ana-lyzed in this study, a positive correlation (p ≤ 0.05) wasidentified between abundances of Epsilonproteobacteriaand Bacteroidetes across watershed sites. Abundancesfor both classes were found to be higher (p ≤ 0.0476) inFig. 3 Relative abundance of bacterial and viral communities characterized using a metagenomic approach in watershed sites. UPL urbanpolluted, UDS urban downstream, AUP agricultural upstream site, APL agricultural polluted, ADS agricultural downstream, PUP protected upstream,PDS protected downstream. Taxonomic classes of bacteria (a), taxonomic groups of DNA viruses (b), and taxonomic groups of RNA viruses (c)Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 12 of 19agricultural and urban sites compared to protected water-sheds. Both of these classes of bacteria have been reportedto play an important role in coastal and ocean watersby reducing sulfidic conditions in sediments, anoxicwater columns, and oxygen minimum zones [123, 124].These shifts in bacterial population structure may rep-resent an ecological succession in community struc-ture as a response to the conditions in the impactedwatersheds [123].While relative abundances were used to compensatefor library variability and read numbers for each library,clear differences were observed among taxa within thesame fraction. For instance, a comparison of taxa abun-dances generated by the bacterial amplicon and metage-nomic sequencing shows that the 16S rRNA amplicondata predict lower abundance of certain bacterial groups,such as Alpha- and Betaproteobacteria, relative to cpn60and metagenome sequencing (Figs. 2 and 3). In contrast,cpn60 sequencing predicted a lower Bacteroidetesabundance compared to 16S rRNA and metagenomesequencing. Moreover, classes such as Planctomycetacia,Chloroflexi, and Verrucomicrobiae represented ~50 %of other taxa using cpn60 sequencing, while 16S rRNAsequencing identified the same classes in approxi-mately ≤1 % among other taxa in most samples.E. coli, a fecal indicator organism, was further com-pared with other taxa using amplicon and metagenomesequencing. E. coli abundance is difficult to measureusing 16S rRNA due to the high 16S sequence similarityamong genera in the Enterobacteriaceae family. How-ever, significant correlations were detected across sitesfor E. coli abundance between 16S rRNA and cpn60amplicons (p = 0.0104) and cpn60 and metagenomic li-braries (p = 0.0018) (Table 3). Metagenome data revealedother taxa such as Polynucleobacter, Arcobacter, Methy-lotenera, Flavobacterium, Pseudomonas, and Bacteroideswere found in higher relative abundance compared to E.coli in urban and agricultural influenced watersheds.Some of these alternative indicators have been previ-ously reported in urban-impacted watersheds [7]. Poly-nucleobacter was abundant in amplicon and bacterialmetagenome of impaired watersheds. The ubiquity ofthis genus has been reported in freshwater lakes and riv-ers associated to ecological diversification [125, 126] andmay be an indicator of impacted environments.Characterization of viruses in watershedsFor DNA virus metagenomic libraries, the rate of taxo-nomic classification to viral taxa was between 7.81 %(UPL) and 18.89 % (PDS), while for RNA viruses, thisnumber ranged from 1.2 % (UPL) to 17.06 % (APL)(Additional file 1: Table S6). Taxonomic analysis revealedsix major groups of DNA viruses and four major groupsof RNA viruses (Fig. 3c), with Microviridae andTombusviridae being the most well represented groupsof DNA and RNA viruses, respectively. The majority ofassigned viral DNA sequences (82 %) corresponded toviruses or phages infecting bacteria such as Microviri-dae, Siphoviridae, Myoviridae, and unclassified DNA vi-ruses and phages, while 64 % of assigned RNA virussequences were identified as algal and plant pathogenssuch as Tombusviridae, Picornavirales, Sobemovirus, andsatellite RNA viruses. These families and groups havepreviously been reported as abundant in other fresh-water ecosystems that are rich in organics [73, 127], sug-gesting trophic status or productivity may influence viralhosts and therefore affect viral community structure. Inthis context, the remaining groups of DNA viruses in-cluded Circoviridae and Parvoviridae, which are knownto contain plant and animal viruses [81, 128, 129].Despite efforts to eliminate DNA from the viral RNAfraction, a significant number of DNA virus sequenceswere detected in the viral RNA fraction across all sites.These sequences were identified as belonging to groupssuch as Caudovirales, Circoviridae, Microviridae, classi-fied and unclassified phages infecting prokaryotes, andunclassified ssDNA viruses. A relatively small percentage(~5 %) of RNA viruses were identified in the viral DNAfraction. Other researchers have also reported DNA andRNA viruses in the opposite fractions [79, 131]. A prob-able explanation for these observations may be relatedto DNA and RNA reverse transcribing viruses combinedwith representational deficiencies in existing viral data-base [11, 130, 131].Sequencing of g23 revealed ~92 % of sequencesmatched the Myoviridae family including Enterobacteriaphages, T4-like viruses, and Klebsiella phages. Moreover,the relative abundance of RdRp sequences indicatedPicornavirales as the most abundant group across siteswith JP-B-like viruses accounting for ~90 % of the Picor-navirales sequences detected. RdRp amplicons in AUPrevealed a more homogeneous distribution of picorna-like viruses compared to the other watersheds (Fig. 2).Other members of this picorna-like superfamily wereDicistroviridae, Marnaviridae, Iflaviridae, and Secoviridae,which have been reported as insect and algae-infecting vi-ruses [11, 132]. Compared to the other microbial fractions,a majority of metagenome viral sequences remained unclas-sified suggesting that a larger, more complete viral databaseis needed in order to identify these unknown viruses. Theseresults are largely congruent with other studies in aquaticecosystems [27, 81, 133, 134], which have also reported amajority of sequences to have unknown origins.Assessment of community structure among microbialfractionsSequencing analysis revealed the presence of larger andsmaller organisms within each size fraction, but thisUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 13 of 19percentage was minimal within the bacterial and viralDNA fractions. From the bacterial fraction sequencingreads, 96.3 % were associated with bacteria and the re-mainder classified as Archaea (2.2 %), Eukaryotes(1.3 %), and viruses comprised less than 0.2 % (notPhiX sensu lato). Ribosomal RNA was detected in viralRNA fraction with percentages ranging from 5.3 to24.5 %. Ribosomal RNA in the viral DNA fraction wasfound in less than 1 % of the reads. These ribosomalRNAs belonged to bacterial and eukaryotic groups alsofound in the bacterial metagenome. We hypothesizethat free ribosomes from lysed cells (including fromthe viral lytic cycle) could persist in the environmentand would pass through all the filters. Although RNAlevels were low or below the detection limit in the viralfraction, the random amplification process could haveamplified this material [135]. Also, eluates were nottreated with RNase due to the potential degradation ofsome RNA viruses [81, 136, 137].Additionally, the Enterobacteria phage PhiX sensu lato,a control used during sequencing, was detected amongthe viral metagenomic and amplicon libraries, but not inbacterial metagenomic libraries. PhiX contamination aver-aged ~4 % in the g23, RdRp, and viral RNA libraries and~32 % in the viral DNA libraries. This adapter-ligated con-trol was identified in ~0.34 % of the reads in other non-viral amplicon libraries such as ribosomal RNA (18Sand 16S rRNA genes) and ITS. Mock communities(bacterial and viral) and amplicons (16S rRNA, g23, andRdRp) used as sequencing controls were further ana-lyzed for possible PhiX contamination. Similar to bac-terial libraries from environmental samples, no PhiXsensu lato was found in mock bacterial libraries. In viraland amplicon control libraries, less than 1 % of the totalreads were assigned to PhiX. A probable explanationmay be related to random cluster dispersal that may re-sult in cluster overlap during Illumina sequencing[138]. The high percentage of PhiX in the viral DNA li-braries suggests that PhiX (a coliphage) was indeed in-fecting bacteria such E. coli in these samples [139, 140].A correlation analysis performed between the abun-dance of PhiX observed in samples and the relative se-quence abundance of E. coli (Table 3) did not detectany significant differences. While the occurrence PhiXsensu lato in viral DNA reads may reflect their actualpresence in the environment, reads associated to thisbacteriophage were excluded from the results describedhere as we cannot determine how much could be dueto contamination. An alternative approach would havebeen to skip the addition of PhiX to the libraries or usea different adapter-ligated control in order to corrobor-ate for the presence of these coliphages in the samples.Gene function analysis and compositionBesides improving to taxonomic analysis, metagenomicsequencing has the advantage of enabling the analysis ofFig. 4 Heat map of functional categories for bacterial, viral DNA, and viral RNA metagenomes across watershed locations. UPL urban polluted,UDS urban downstream, AUP agricultural upstream site, APL agricultural polluted, ADS agricultural downstream, PUP protected upstream, PDSprotected downstream, Bac bacteria, vDNA viral DNA, vRNA viral RNAUyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 14 of 19the functional gene content of the microbial community.Comparison of gene function abundances in the bacterialfraction using SEED subsystem classifications revealed nomajor differences across sites (Fig. 4). Genes with un-known function that are hypothesized to have similarfunctions based on their locations across genomes (clus-tering-based subsystems) made up most of the bacterialannotations (~12.23 %), followed by genes involved in pro-tein metabolism (~7.35 %). Phage-associated genes, in-cluding those coding for capsid proteins and terminases,were the most abundant functional annotations in theviral DNA fraction (Fig. 4). These genes are major compo-nents of the phage DNA packaging machinery [141, 142].Notably, in the RNA fraction, retron-type reverse tran-scriptase genes (RNA metabolism category) were foundin higher relative abundance in AUP and urban sitescompared to other sites (Fig. 4). This reverse flow ofgenes is considered a fingerprint of viral replication inany system [143].As parasites of their hosts, viruses enter and hijacknatural cellular process [144]. Universal glycoproteinssuch as GTPases made up significant proportions of theprotein metabolism within the viral DNA fraction.Higher abundance of genes involved in carbohydratemetabolism (mainly ribokinases) was observed withinthe viral RNA fraction. The acquisition of cellular genesmay reflect host-defense mechanisms against viruses andutilization of the host machinery for the viruses to repli-cate. Consistent with the findings from the taxonomiccharacterization, where DNA viruses were found in theviral RNA fraction, analysis of putative functions re-vealed DNA virus-associated genes (encoding for phagecapsids and scaffolds) in some of the viral RNA samples.Overall, a wide variety of taxa and genes were pre-dicted using amplicon and metagenomic approaches. Asdiscussed above, this variability in microbial communi-ties can be attributed to a combination of the surveymethods, organism/particle sizes, anthropogenic affects,and environmental conditions.ConclusionWe have developed a method for separating differentsized microorganisms (eukaryotes, bacteria, and viruses)from water samples and comprehensive characterizationof the water microbiota. So far, few studies haveattempted to characterize multiple microbial domainsfrom the same environmental sample due to technicalchallenges, the effort required, and the cost. The use ofa systematic size fractionation approach enabled enrich-ment of a particular fraction and minimized noise duringmetagenomic sequencing from larger fractions to thesmaller ones. While multiple associations were observedbetween classes and sites, we have highlighted only themost representative findings as the focus was to developa robust methodology for extensive watershed micro-biome analysis. The use of metagenomics to characterizemicrobial communities provides insights not only intothe wider range of microbial eukaryotic, bacteria, andviral taxa present in watersheds but also for further ana-lysis of the functional gene complement in these micro-bial communities. Note that a large proportion of viralamplicon and metagenomic sequence data remained un-assigned, supporting the need for further study of viraldiversity and development of viral sequence databasesfor reference-based analysis. A year-long, large-scalewatershed metagenomic project (http://www.ncbi.nlm.nih.gov/bioproject/287840) has employed the methodsdescribed here, and this report describes the early find-ings from this study. This larger project aims to furthercharacterize profiles of microbial eukaryotes, bacteria,and viruses, combined with physical, chemical, and bio-logical indicator data, in multiple watersheds in BritishColumbia with the ultimate goal of discovering newbiomarkers to monitor water quality. We hope that themethods described here and the accompanying prelim-inary data will support other similar holistic analyses ofwater microbial communities and not only improveour understanding of the complexities of the watermicrobiome but also further our ability to protect ourwatersheds.Additional filesAdditional file 1: Word document includes table of contents forsupplemental results, discussion, and detailed methods. Supplementaltables (Tables S1–S9) and figures (Figures S1–S5) are also included.(DOCX 785 kb)Additional file 2: Excel file contains output data from MG-RAST, mPUMA,and MetaVir reported in this study. Singletons have been removed from theanalysis. (XLSX 1941 kb)Abbreviationscpn60: chaperonin-60; FCM: flow cytometry; GCN: gene copy number;ITS: internal transcribed spacer; g23: major capsid gene; qPCR: quantitativePCR; RdRp: RNA-dependent RNA polymerase; rRNA: ribosomal RNA;TFF: tangential flow filtration.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionsMUD performed the experiments, analyzed the data, prepared the figuresand/or tables, and wrote and reviewed the drafts of the manuscript. MCperformed the experiments and reviewed the drafts of the manuscript. BLCconducted cpn60 amplicon generation, analyzed the data, and reviewed thedrafts of the manuscript. MAC analyzed the data, contributed the analysistools, and reviewed the drafts of the manuscript. JFF performed theexperiments, analyzed the data, and reviewed the drafts of the manuscript.MAP contributed the analysis tools and reviewed the drafts of themanuscript. TVR contributed the analysis tools, prepared the table, andreviewed the drafts of the manuscript. JEH, CAS, FSLB, JIR, NAP, and PKCTdesigned the experiments, contributed the analysis tools, and reviewed thedrafts of the manuscript. All authors read and approved the final manuscript.Uyaguari-Diaz et al. Microbiome  (2016) 4:20 Page 15 of 19AcknowledgementsA special acknowledgement to Brian Auk, Belinda Wong, and the staffmembers in the Environmental Microbiology Laboratory at the BC PublicHealth Laboratory (BCPHL) for their advice and support. The authors wouldlike to thank Dr. William Hsiao, Dr. Martin Petric, Dr. Iris Luo, Kirby Cronin,Vincent Montoya, Louella D'Silva, David Lawrence from BCPHL, andRaymond Lo from Simon Fraser University (SFU) for their help with thesample collection, experimental controls, sample processing, and qPCR data.We also thank Alan McNabb and the staff members in the MolecularMicrobiology and Genomics Program at the BCPHL for their PCR andsequencing expertise. A special acknowledgement to Majid Hojjati-Najafabadi (SFU) for his help with the Tableau software license (version 9.0).FundingThis work was funded by Genome BC and Genome Canada grant no.LSARP-165WAT, with major support from the Simon Fraser UniversityCommunity Trust Endowment Fund and additional support from the PublicHealth Agency of Canada. MUD was supported by a Mitacs Acceleratefellowship. TVR is supported by an NSERC Canada Graduate scholarship, andMAP has been supported by scholarships from the CIHR/MSFHR BioinformaticsTraining Program and NSERC.Author details1British Columbia Public Health Laboratory, British Columbia Centre forDisease Control, Vancouver, BC V5Z 4R4, Canada. 2South Kensington Campus,Imperial College London, Sir Ernst Chain Building, London SW7 2AZ, UK.3Department of Earth, Ocean and Atmospheric Sciences, University of BritishColumbia, Vancouver, BC V6T 1Z4, Canada. 4Department of Microbiology andImmunology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.5Department of Botany, University of British Columbia, Vancouver, BC V6T1Z4, Canada. 6Department of Veterinary Microbiology, Western College ofVeterinary Medicine, University of Saskatchewan, Saskatoon, SK S7N 5B4,Canada. 7Department of Molecular Biology and Biochemistry, South ScienceBuilding, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. 8IntegratedMicrobial Biodiversity Program, Canadian Institute for Advanced Research,Toronto, ON M5G 1Z8, Canada. 9Department of Pathology and LaboratoryMedicine, Faculty of Medicine, University of British Columbia, Vancouver, BCV6T 1Z4, Canada. 10Department of Pathology, Sidra Medical and ResearchCenter, PO Box 26999, Doha, Qatar.Received: 17 November 2015 Accepted: 4 April 2016References1. 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