Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform

Journal of Microbiological Methods 91 (2012) 80–88

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Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform Andrew S. Whiteley a, b,⁎, Sasha Jenkins b, c, Ian Waite b, c, Nina Kresoje d, e, Hugh Payne f, Bruce Mullan f, Richard Allcock d, e, Anthony O'Donnell b, c a

Centre for Ecology and Hydrology, Wallingford, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, United Kingdom School of Earth and Environment, Faculty of Natural and Agricultural Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Western Australia, Australia Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley 6009. Western Australia, Australia d School of Pathology and Laboratory Medicine, The University of Western Australia, 35 Stirling Highway, Crawley 6009, Western Australia, Australia e Pathwest Laboratory Medicine, Royal Perth Hospital, Wellington Street, Perth 6000, Western Australia, Australia f Animal Science, Department of Agriculture and Food Western Australia, 3 Baron-Hay Court, South Perth 6151, Western Australia, Australia b c

a r t i c l e

i n f o

Article history: Received 25 June 2012 Received in revised form 9 July 2012 Accepted 9 July 2012 Available online 29 July 2012 Keywords: Ion Torrent PGM rRNA Community diversity Barcode multiplex Metagenome

a b s t r a c t Here we demonstrate a cost effective and scalable microbial ecology sequencing platform using the Ion Torrent Personal Genome Machine (PGM). We assessed both PCR amplified 16S rRNA and shotgun metagenomic approaches and generated 100,000+ to 1,000,000+ reads using ‘post-light’ based sequencing technology within different sized semi-conductor chips. Further development of Golay barcoded Ion Tags allowed multiplex analyses of microbial communities with substantially reduced costs compared with platforms such as 454/ GS-FLX. Using these protocols we assessed the bacterial and archaeal dynamics within covered anaerobic digesters used to treat piggery wastes. Analysis of these sequence data showed that these novel methanogenic waste treatment systems are dominated by bacterial taxa, in particular Clostridium, Synergistia and Bacteroides that were maintained as a stable community over extended time periods. Archaeal community dynamics were more stochastic with the key methanogenic taxa more difficult to resolve, principally due to the poor congruence seen between community structures generated either by nested PCR or metagenomic approaches for archaeal analyses. Our results show that for microbial community structure and function analyses, the PGM platform provides a low cost, scalable and high throughput solution for both Tag sequencing and metagenomic analyses. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Over the past 10 years our understanding of microbial diversity and function in complex environments has increased significantly. This is primarily a result of the introduction of next generation sequencing (NGS) (Lozupone and Knight, 2007; Sogin et al., 2006). Both PCR based analysis of 16S rRNA and shotgun metagenomic studies have been used recently to characterise soils (Fierer et al., 2011), oceans (Caporaso et al., 2011), the atmosphere (Bowers et al., 2011) as well as the human microbiome (Kuczynski et al., 2011). Prior to the advent of NGS, the high throughput genetic analysis of complex microbial community samples was only possible using low resolution ‘fingerprinting’ technologies e.g. (Griffiths et al., 2011) or Sanger sequencing at extremely high cost e.g. (Rusch et al., 2007). However, with NGS we now have the potential to fully sequence, at high taxonomic resolution, most known habitats on Earth (Gilbert et al., 2010). ⁎ Corresponding author at: Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, United Kingdom. Tel.: +44 1491 692640. E-mail address: [email protected] (A.S. Whiteley). 0167-7012/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.mimet.2012.07.008

At the vanguard of this revolution in microbial ecology are core and ancillary technological advances that have enabled the high throughput solutions essential for studying microbial community dynamics and function. These include the original pyrosequencing developments for high throughput sequence generation (Margulies et al., 2005), developments in multiplex analyses through barcoding (Hamady et al., 2008), step changes in sequence outputs to ultrahigh throughput systems (Bartram et al., 2011; Caporaso et al., 2012) and substantial increases in the ability to analyse and handle data outputs (Caporaso et al., 2010; Lozupone and Knight, 2005; Meyer et al., 2008). Until relatively recently, a number of factors have limited the uptake of large scale sequencing capabilities within personal laboratories. These include the need for ‘facility’ level support for technically complex platforms and the need for large initial equipment purchase and ongoing analysis costs (Glenn, 2011). Further, in many cases a factor best termed the ‘fixed system issue’ means that many existing NGS technologies depend on key, but fixed, light detection systems. Thus, as technology improves, flexible upgrades on detection systems are rare and new sequencing platforms have to be purchased to keep pace with new NGS developments and increased outputs (Glenn,

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2011). These factors prevent many small to medium laboratories from adopting existing high throughput technologies ‘in house’ and rely instead on central facilities. This is a particular problem for many microbial ecology laboratories where the need for replication and longitudinal studies means relatively high outsource costs and results in a situation where routine access to the technology is reduced. However, the availability of low cost, high throughput systems would add value to existing ‘facility’ based environmental sequencing efforts and to the ‘democratisation’ of sequencing for the good of global microbial ecology (Caporaso et al., 2012). Recently, a different strategy of ‘ion’ sequencing, commercially available within the Ion Torrent Personal Genome Machine (PGM) (Life Technologies), has received considerable attention. PGM sequencing is light independent where sequence composition is determined by measuring pH changes due to hydrogen ion liberation as nucleotides are incorporated during strand synthesis in picolitre wells (Rothberg et al., 2011). Using integrated circuits to measure pH changes to identify base incorporation removes the need for expensive light detection systems, substantially reduces costs and, theoretically, is infinitely scalable; since the number of sequences obtained simply equates to the physical dimensions of the integrated sensor (Glenn, 2011). However, since the PGM is a relatively new technology, and approaches sequencing in a different way to standard NGS platforms, there has been much speculation as to the suitability of the platform for microbial ecology. This includes the quality and extent of the output (the number and read length of sequences) as well as the compatibility with downstream data analysis pipelines and the ability to multiplex microbial community analyses. To address these questions we have investigated PGM sequencing for microbial ecology and examined bacterial and archaeal community dynamics in a covered anaerobic pond (CAP) used to treat piggery waste. CAPs represent a novel and low cost option for treating effluent ponds and are being used to provide a cost effective anaerobic digester by covering the ponds with a geosynthetic material. CAP systems have the benefits of renewable energy generation via biologically mediated methanogenesis (with concomitant GHG mitigation). Here, we have developed the PGM sequencing protocols and used these to investigate the community structures, temporal stability and major taxa of these CAP systems. 2. Materials and methods 2.1. Site description, sampling and DNA extractions A covered anaerobic pond (CAP) at Medina Research Station (MRS), Western Australia (GPS geocoder: latitude −32.223000, longitude 115.805801) was used as the source of samples to investigate both the microbial production of methane by anaerobic digestion of piggery waste and the efficacy of community structure analyses using PGM sequencing. Effluents from pig holding pens were collected and solids were mechanically screened and removed prior to transferring the remaining wastewater into a storage holding tank. Subsequently, wastewater was transferred to a geosynthetic membrane covered anaerobic pond (CAP) digester at a rate of 75,000 L per week for downstream biological remediation. Microbial sampling of the CAP was performed by suction collection using a 12 V marine grade bilge pump connected to a PVC hosepipe. The hosepipe was placed into the access port of the CAP and ran for 5 min to flush the sampling line, followed by a collection of 1 L of sample into sterile containers. Subsequently, the container was well mixed and sub-samples of mixed liquor with suspended solids (MLSS) removed for downstream DNA extraction and molecular processing. To determine both archaeal and bacterial community dynamics within the anaerobic digester over an extended period, monthly samples were removed from the CAP commencing in April 2010 over a 10 month period. DNA for PCR analyses of the 16S ribosomal RNA (rRNA) gene was extracted from 1 mL sub-samples of effluent after

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centrifugation in a microfuge to pellet microbial biomass from the MLSS. DNA was extracted using the MoBio Powersoil DNA isolation kit (Geneworks, Australia), utilising beat beating and column purification, according to the manufacturer's guidelines. Extracted DNA was quantified and checked for purity at A260/280 nm (Nanodrop, Thermo Fisher Scientific, USA) prior to storage at − 20 °C. 2.2. PCR based analysis using Ion Tags Bacterial and archaeal 16S rRNA genes were amplified by polymerase chain reaction (PCR) from DNA samples using a range of V3 and V6 oligonucleotide primers specific for domain bacteria and archaea (Supplementary Table S1a and S1b). Specifically, all forward core primers (where ‘core’ refers to the original unmodified 16S rRNA amplification primer) were modified by the addition of a PGM sequencing adaptor, a ‘GT’ spacer and unique error correcting Golay barcode (Hamady et al., 2008) as described in Tables S1a and S1b, to allow multiplex analyses where required. Two regions of the bacterial 16S rRNA were used for PGM sequencing, since they yielded two different size fragments which could be tested using the two length chemistries currently available (100 and 200 b.p. variants). First, we amplified ca. 100 b.p. of the V6 region using the Golay and PGM adapter modified versions of the core primers A967F and 1046R (Table S1a). Amplification conditions employed were according to previously published protocols (Sogin et al. (2006)). Second, we amplified a standard 200 b.p. V3 region using Golay barcode and Ion Torrent adapter modified core primers 341F and 518R (Muyzer et al., 1993), using amplification conditions described previously (Jenkins et al., 2010). Following amplification, all PCR products were checked for size and specificity by electrophoresis on 2.5% w/v agarose, gel purified and adjusted to 10 ng μL−1 using molecular grade water and pooled equally for subsequent sequencing. For Archaea, a nested PCR approach was used to amplify 16S rRNA genes due to previous observations of low target abundance within this system. The first round PCR was performed using primers Arch 46F and Arch 1017R (Barns et al., 1994; Ovreas et al., 1997) followed by a second round of PCR using Golay and linker modified core V3 primers Arch 344F and Univ 522R (Suppl. Table S1b; Amann et al., 1995; Raskin et al., 1994) with target sites internal to the first primer pair. For the first round PCR, template DNA was amplified in 30 μL volumes containing 0.33 pM of each primer, 0.58 mM of dNTPs, 1X PCR buffer 0.15 mM MgCl2 and 1 U of Dynazyme EXT. The reaction conditions were 94 °C for 5 min (initial denaturation) followed by 30 cycles for 1 min (denaturation); 42 °C, 1 min (annealing); 72 °C, 2 min (extension) and a final extension at 72 °C. The nested PCR reaction was performed using a 1 μL aliquot for the first round product as the template for the second round of PCR. The PCR was set-up in 50 μL volumes using 0.2 pM of each primer, 0.2 mM of dNTPs, and 1X PCR buffer (Thermo Fisher Scientific, Victoria), 1.5 mM MgCl2 and 2 U of Phusion High Fidelity DNA Polymerase (Thermo Fisher Scientific, Victoria). Prior to sequencing, all amplicon types were assessed for fragment size distribution and DNA concentration using a Bioanalyser 2100 (Agilent Technologies, USA). The samples were adjusted to a final concentration of 10–15 pM and attached to the surface of Ion Sphere particles (ISPs) using either an Ion Xpress Template 100 or 200 kit (Life Technologies, USA) according to the manufacturer's instructions. Manual enrichment of the resulting ISPs resulted in >95% templatedISPs. Templated-ISPs were sequenced on either “314” (10 Mb.p.) or “316” (100 Mb.p.) micro-chips using the Ion Torrent Personal Genome Machine (Life Technologies, USA) for 65 cycles (260 flows) or 130 cycles (520 flows), resulting in an expected average read length of >100 b.p. for Ion Express Template 100 chemistry or >220 b.p. for the Ion Express Template 200 chemistry. After sequencing, the individual sequence reads were filtered within the PGM software to remove low quality and polyclonal sequences. Sequences matching the PGM 3′ adaptor

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were also automatically trimmed. All PGM quality filtered data were exported as FastQ files, split into constituent *.fasta and *.qual files and subsequently analysed using the QIIME pipeline as described previously (Caporaso et al., 2010) implemented within the Amazon EC2 image. Briefly, within the QIIME environment, sequences were filtered based upon user defined size criteria, default quality scores followed by OTU picking at 94% similarity and then representative OTU's assigned phylogenetic identities using the RDP_22 release database. All analyses were run using the ‘core QIIME analyses’ routine at the N3PHELE website (http://www.n3phele.com). 2.3. Metagenome analysis For selected samples, we also compared PCR-independent metagenomic based analyses of community diversity. Total nucleic acids were extracted using the phenol-chloroform-CTAB method (Griffiths et al., 2000) and re-suspended in 30 μL of molecular grade H2O. RNA was removed from the extracts using RNase I prior to sequencing. Subsequently, total genomic DNA was sheared using a Covaris S2 sonicator (Covaris, USA) and ligated to sequencing adapters according to the manufacturer's instructions. Sequencing was performed using Ion Express Template 200 chemistry, quality filtered then exported in FastQ format. Resulting sequencing data sets were uploaded to the Metagenome Rapid Annotation using Subsystem Technology (MG-RAST) server (http://metagenomics.nmpdr.org/), checked for low-quality reads prior to dereplication, annotation, assignment of metabolic function and phylogenetic identification as described previously (Meyer et al., 2008). 3. Results 3.1. Read output and base positional quality from 314 and 316 PGM Chips We first sought to quantify the absolute number of PCR amplified 16S rRNA sequence read outputs and to categorise those reads of sufficient quality for downstream phylogenetic analyses, from both 314 (10 Mb.p) and 316 (100 Mb.p) PGM chips. For both the 314 and 316 chips we assessed the total number of reads generated (as a proxy for active sequencing wells on each chip) and demonstrated that the mean raw read output exceeded 650,000 reads for 314 chips (manufacturer specifies a minimum output of 100,000 reads) and c.a. 2.5 million reads for 316 PGM chips (minimum specification 1 million reads, Fig. 1). For QIIME analysis, where additional filters can be applied to sequences prior to phylogenetic analysis, we used a quality filter of Q20 and appropriate size inclusion filters, depending upon fragment size (e.g. 100–130 b.p. for V6 PCR and 140–180 b.p. for V3 PCR). Applying these filters we obtained a mean output for 314 chips of 350,000 reads and 1.2 million reads for 316 chips (‘Quality Filtered’ reads, Fig. 1). Therefore, when compared to the manufacturer's specifications, we averaged a 3.5 fold increase in phylogenetically suitable reads for 314 chips and a 20% increase in reads from 316 PGM chips. Having determined that the average outputs from each of the chips provided enough sequence information of sufficient quality, we examined the positional read quality distribution across sequences, an important factor required for accurate phylogenetic analysis. We tested this against two different, but widely used, PCR fragments (the 100 b.p. V6 and 200 b.p. V3 regions) and applied the two currently available sequencing chemistries; the 100 b.p. and 200 b.p. variants. After removal of PGM linker primers, Golay barcode and forward amplification primer during QIIME analysis, the read quality across the phylogenetically informative sequence length was used to determine the suitability of fragments for downstream analyses (Fig. 2). For the short V6 PCR fragment (ca. 100b.p.), we obtained approximately

Fig. 1. Mean read output (n = 3) for both 314 (10 Mb.p.) and 316 (100 Mb.p.) PGM chips ranked according to expected output based upon manufacturer's specifications total reads after initial PGM filtering for polyclonality and quality (raw) and QIIME quality filtering at Q20 (quality filtered).

80 b.p. of sequence post PCR primer with a median quality score of Q20 using the 100 b.p. chemistry. The 200 b.p. chemistry provided similar read lengths for the V6 primer (Fig. 2A) but showed a higher overall positional quality score (median Q25, Fig. 2B). In contrast, sequencing the larger V3 (ca. 200 b.p.) fragment with the 100 b.p. chemistry, resulted in a gradual degradation of sequence quality across the whole length of the sequence (Fig. 2C) with quality scores as low as Q10 by the termination of the fragment. However, sequencing the longer fragment with the longer read chemistry (Fig. 2D), meant that the full length sequence could be recovered with a median quality score of Q25 across the whole read with no appreciable sequence degradation. Interestingly, we observed consistent regions of the sequence where the sequence quality rapidly declined then recovered over ca. 10–15 b.p. This was most evident around the 40 b.p. region for most fragment and chemistry types and may be a product of the chemistry itself, since it occurred in different amplicon types (e.g. V6 versus V3) at the same sequencing position (Fig. 2).

3.2. Phylogenetic comparison of long versus short reads Using the shorter V6 and longer V3 fragments generated from bacterial specific PCR amplifications, sequenced with 200 b.p. chemistry, we determined the efficacy of the QIIME pipeline to automatically ‘batch’ characterise both the short and longer reads within a phylogenetic framework using minimal user intervention, as would be required when generating large numbers of datasets (Fig. 3). As one might expect, poor phylogenetic identification was obtained with the shorter V6 fragments, where 95% of the total reads were only classified to the bacterial root, with low percentages of the remaining sequences identified to class level taxa (Fig. 3A). Using the longer V3 fragments, QIIME accurately classified over 80% of the sequences to known taxa (usually genus levels but class levels are presented here for ease of data visualisation), with around 20% classified only to the bacterial root (Fig. 3B). Of the 80% of classified sequences, these data indicated that Clostridia were the dominant class of bacteria, followed by the Bacteroidia, which together accounted for over half of the sequences classified. Synergistia and 17 other class level taxa comprised the remaining taxa within the community. Identification of Clostridia and Bacteroidia as the dominant bacterial populations is consistent with other work done within these systems where 16S rRNA gene amplification, denaturing gradient gel electrophoresis (DGGE), cloning and sequencing also revealed Clostridia and

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Fig. 2. Positional quality scores averaged across all of the 16S rRNA reads for 100 b.p. (V6) and 200 b.p. (V3) PCR amplified fragments analysed using both 100 and 200 b.p. sequencing chemistries. Raw read output was size filtered to required fragment size prior to calculating quality scores. Note, position 1 corresponds to the first base distal to the amplification primer where PGM primer, Golay barcode and PCR amplification primer at the 5′ end were removed before analyses. Scores were derived from >100,000 bases at each position.

Bacteroidia as predominant members of the microbial community (data not shown).

3.3. Multiplex analyses of bacteria and archaeal temporal community structure within the CAP To be of value for microbial ecology it is essential that the PGM platform is able to accommodate multiplexed analyses. We used multiplexing to determine the bacterial and archaeal community structures in a covered anaerobic pond over a 10 month period by targeting the V3 amplification region. For each bacterial or archaeal analysis (20 multiplexed samples of bacterial or archaeal PCRs per 316 chip), we recovered an average of 50,000 reads, after QIIME quality filtering and library splitting, that were suitable for subsequent phylogenetic analysis. For bacterial communities, multiplex analyses confirmed the previous observations (Fig. 3), that of the taxa identified, Clostridia were the dominant community members followed in this sample set, by Synergistia and Bacteroidia. Over a 10 month period, these three taxa dominated the community composition and maintained a relatively stable community composition (Fig. 4), represented by mean abundances over the period of 25% ±6% (Clostridia), 12% ± 4% (Synergistia) and 21% ±5% (Bacteroidia). Another 16 class level taxa, ranging in abundance from 0.3% to 2.6% and including Mollicutes, Alpha-, Beta-, Gamma-, Delta- and Epsilonproteobacteria as well as Actinobacteria were also detected. These temporal analyses indicated, therefore, that the bacterial community within the CAP, whilst relatively

diverse, was dominated by a temporally stable community of three main taxa, supplemented by relatively low abundance groups. Archaeal abundances, in contrast, tended to be more skewed towards one key taxon and varied stochastically with time (Fig. 4). Family level analyses of multiplexed archaeal samples indicated that sequences associated with the Methanocorpusculaceae dominated the sequences recovered and in most instances accounted for at least 50% of the total sequences obtained (Fig. 4). Other groups, of lower abundance, included the Methanobacteriales and Methanomicrobiales (ca. 20% and 10% of sequences recovered over the temporal analyses, respectively). The dynamics between the three groups varied over time with the Methanocorpusculaceae increasing markedly in relative abundance within 2 months of the pond being covered (up to 90% of sequences), but then decreased over the next 6 months before recovering back to levels seen at the beginning of the sampling (Fig. 4). 3.4. Comparison of PCR and metagenome assessments of community diversity To compare community composition from both a PCR based and PCR independent standpoint and to examine the congruence between approaches, we generated approximately 4 million reads averaging 200 b.p. in length from the CAP using shotgun metagenomics. Phylogenetic reconstruction based upon functional gene phylogenies within MGRAST revealed that of the community DNA, 87% was associated with bacterial DNA whilst archaeal DNA represented 10% and Eukaryotes 2% of the total community DNA, indicating the CAP system was dominated by bacterial taxa (Fig. 5A).

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Fig. 3. Comparison of phylogenetic analyses of A) short (100 b.p.) V6 region and B) longer (200 b.p.) V3 regions for bacterial community composition of wastewater samples.

For bacterial identities based upon functional gene phylogenies, metagenome reconstruction indicated general agreement for the presence of previously identified dominant groups such as Clostridia and Bacteroidia, but did not identify Synergistia as a significant component of the microbial community when compared to PCR based assessments (Fig. 5B). Furthermore, metagenome analyses indicated that both Delta- and Gammaproteobacteria were more prevalent in the CAP system than was observed using the PCR based analysis (Fig. 5B and 4A). Notwithstanding these differences there was reasonably a good agreement between the approaches with many of the same groups recovered in both the PCR and metagenome analyses including all classes of Proteobacteria, Actinobacteria, Spirochaetes, Cyanobacteria and Bacilli. However, it was clear that relative taxon contributions to community structure varied between the two assessments for many of the taxa identified. In contrast to the bacterial analysis, archaeal community reconstruction by metagenomic analyses indicated a very different picture of community structure at the family level when compared to PCR based sequencing (Fig. 5C). Metagenomic analysis suggested that the CAP was dominated by members of the Methanosarcinaceae, as opposed to the Methanocorpusculaceae as indicated by PCR based methods. Indeed, the latter family only represented a small percentage (b 5%) of the metagenome analysis, whereas the community DNA analysis indicated groups such as the Methanosarcinaceae and Methanosaetaceae accounted for almost 75% of the DNA sequenced. There were, however, similarities in other groups with taxa such as the Methanobacteriales and Methanomicrobiales represented at similar

levels (10–20% sequence coverage) to those observed previously with PCR based methods. 4. Discussion 4.1. Scalable sequence output The Ion Torrent PGM platform is currently one of the lowest cost next generation sequencers capable of multi-million read level outputs. Further, through the use of ‘post-light’ chemical sensor technology, sequence outputs can be scalable through different chip sensor sizes and allows sequence turnaround times of only a few hours (Glenn, 2011). Utilising different output PGM chips we routinely generated an average of ca. 350,000 (314 PGM chip) or 1.2 million reads (316 PGM chip) within 8 h. These output levels represented between 1.2 (316 chip) to 3.5 times (314 chip) the manufacturer's stated minimum sequence output and were of sufficient quality for downstream analysis using pipelines such as QIIME (Caporaso et al., 2010). These data suggest that the semi-conductor fabrication process (Rothberg et al., 2011) produces chips with substantially more active wells than are specified and that multi-million level read libraries can be generated for less than USD$500 and that 300,000 to 500,000 reads can be obtained for less than USD$200. Thus, the number and quality of reads are comparable with current 454/GS-FLX/Titanium technologies, but can be obtained for less than one tenth of the reagent costs (Glenn, 2011). Whilst not currently on the same scale as newer generation ultra-high throughput technologies such as Illumina Hi-Seq

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Fig. 4. Multiplex PCR analyses of microbial community structure for both bacteria and archaea using 200 b.p. V3 region analyses over a 10 month period within the covered anaerobic pond. Samples for either A) bacteria or B) archaea were combined after PCR amplification and analysed on a 316 chip yielding an average of >50,000 reads per sample.

and Mi-Seq (Caporaso et al., 2012) the future expansion, to include 10 million (318 PGM chip) and subsequent 500 million read chips, together with 400 b.p. chemistries will make the PGM a valuable addition to the microbial ecologists' suite of tools. We envisage this will primarily take the form of cost effective and rapid routine laboratory sequencing (e.g. 314 PGM chips), replacing relatively laborious analyses such as DGGE (Muyzer et al., 1993) and tRFLP (Griffiths et al., 2011), as well as providing small to medium laboratories with high throughput 16S rRNA and shotgun metagenomic capabilities (e.g. 316 and 318 PGM chips). 4.2. Sequence qualities and capabilities are on a par with other pyrosequencing platforms We generated the first analyses of sequence quality for the Ion Torrent PGM platform across commonly adopted sequencing targets such as the 16S rRNA V3 (Bartram et al., 2011) and V6 (Sogin et al., 2006) regions. Longer read chemistries (200 b.p.) generated sequences with consistent and higher quality scores (Q25 or higher) than the shorter read variants, with quality scores equalling or approaching those obtained for amplicon sequencing using 454 (Tamaki et al., 2011) and Illumina (Caporaso et al., 2012) based technologies. One anomalous sequencing feature we did observe was a lowering of sequence quality across short regions approximately 40 b.p. past the amplification primer; these appeared to be consistent across different

fragment sizes and chemistries. Several inherent errors have been documented for other pyrosequencing platforms, such as homopolymer errors (Margulies et al., 2005) and base calling biases (Erlich et al., 2008). However, the variations in sequence quality observed here did not map to homopolymers and in the absence of information on base call bias, we suggest that a systematic comparison of this and other platforms with the same sequence target may reveal the nature of these sequence quality variations as has been performed for other platforms (Luo et al., 2012). When utilising the QIIME pipeline for phylogenetic identification and reconstruction of microbial communities, we observed that short read (80 b.p.) sequences from the V6 analyses failed to classify the majority (>90%) of sequences past the bacterial root, presumably due to the limited number of characters available for accurate identification (Saitou and Nei, 1987). In general, single direction short fragment (b 100 b.p.) phylogenetic reconstructions are achievable (Werner et al., 2012), but can be problematic depending upon the region analysed (Liu et al., 2007) and generally require less routine analyses, such as mapping to existing phylogenetic trees using computationally intensive algorithms such as FastTree (Price et al., 2010). However, we observed good success with longer fragments (V3) when using QIIME and thus suggest that V3 or adjacent V4 region (Caporaso et al., 2012; Liu et al., 2007) is probably the best candidate for streamlined analyses of microbial communities with the PGM through established pipelines such as QIIME.

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Fig. 5. Bacterial and archaeal community composition of a single wastewater sample assessed by PCR independent methods. Total DNA was sequenced using a 316 chip by shotgun metagenomics and A) domain level (all), B) class level (bacteria) or C) family level (archaea) identifications obtained by taxonomic identity linked to functional gene content by MG-RAST analysis.

Due to low efficiency of phylogenetic classification with short read fragments, we concentrated on the longer sequence fragments obtained from the V3 region using the longer read chemistry and generated upwards of 50,000 quality filtered sequences when multiplexing 20 samples on a single chip. Recent studies indicate that as little as 100 sequences can differentiate beta diversity differences within different communities and increasing sequence coverage from 40,000 to 120,000 sequences per sample had no effect upon the conclusions drawn (Caporaso et al., 2012). Balancing the need for more environmental sampling with the need for sequence coverage suggests that for multiplex analyses (with the possible exception of rare event

analyses) it would be possible to multiplex up to 100 tagged PCR samples on a single 316 PGM chip at approximately USD$5 per sample at a coverage of approximately 10,000 reads per sample. 4.3. Diversity of bacteria and archaea within the CAP Multiplex 16S rRNA analyses indicated that for bacterial communities within the CAP, populations over a 10 month period were dominated by Clostridia, Synergistia and Bacteroidia taxa, with these three taxa remaining relatively stable. These taxa are commonly found in piggery waste treatment systems (Cook et al., 2010; Patil et al., 2010;

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Talbot et al., 2010) and in other anaerobic digesters (Kampmann et al., 2012; Riviere et al., 2009; Supaphol et al., 2011; Tang et al., 2011). They are particularly resilient to perturbation, periods of starvation and fluctuations in pH (Kampmann et al., 2012; Tang et al., 2011) which probably accounts for their temporal stability in these CAPs. Despite the fact that the dominant community members were generally stable over the 10 month period, there were changes in the abundance of rarer community components, notably over the first 3 months after covering, and including the Alpha-, Beta- and Gammaproteobacteria as well as in the class Mollicutes. This probably reflected a period of acclimatisation (Tang et al., 2011; Supaphol et al., 2011) as the microorganisms adapted to the developing anaerobic conditions. This period also coincided with winter and the lower temperatures (12–15 °C) would have slowed microbial activity and growth. The relative abundance of the Deltaproteobacteria increased during the warmer months and additional work (data not shown) has revealed that this group was mostly represented by Syntrophus sp., a fatty acid degrader that only grows syntrophically in partnership with hydrogenotrophic methanogens (Müller et al., 2011). The presence of another butyrate oxidising syntroph, Syntrophomonas (Müller et al., 2011), around the same time suggests an accumulation of butyrate in the CAP during the summer and that its removal is important in maintaining pond stability. PCR based analyses of archaeal samples indicated that the archaeal community varied more stochastically and was primarily dominated by Methanocorpusculaceae, characteristic hydrogenotrophic methanogens which produce methane from H2/CO2, format, and 2-propanol/CO2 (Zellner et al., 1989) and have been reported previously as having a role in the anaerobic digestion of swine wastewater (Hwang et al., 2010). We further detected other hydrogenotrophic taxa by nested PCR analyses, namely Methanobacteriales and Methanomicrobiales, suggesting that hydrogenotrophic methanogens could be primarily responsible for methane production in CAPs, a view consistent with previous observations of the anaerobic digestion of piggery waste (Kim et al., 2010; Patil et al., 2010; Talbot et al., 2010). However, when we examined community diversity from a metagenomic standpoint, we observed significant differences between this and the PCR based analyses of archaeal community composition, much more so than for the bacterial analyses. There is still contention whether congruence is generally observed between metagenomic and PCR based assessments of microbial diversity, principally due to the small number of studies which directly address this issue e.g. (Shah et al., 2011), or through comparative evidence which suggests that similar structures can be obtained e.g. (Kalyuzhnaya et al., 2008a, 2008b). Since the PGM affords cost effective sequencing of both 16S rRNA and metagenomic analyses, we generated co-incident datasets which allowed us to directly address this issue, including both MG-RAST phylogenetic based analyses and by extracting 16S rRNA sequences directly from the metagenome itself (data not shown). Bacterial concordance was similar between these datasets e.g. most key groups were observed and the dominant group the same in both 16S rRNA and metagenome analyses, suggesting some biases (e.g. promotion of Deltaproteobacteria within the metagenome) as has been documented previously (Shah et al., 2011), but similarities nonetheless. However, significant differences for archaeal analyses lead us to believe that nested PCR approaches are likely to be the largest cause of disconnect between the 16S rRNA and metagenome analyses, notwithstanding potential extraction bias (Morgan et al., 2010) or bias from methanogens representation within the MG-RAST database (Meyer et al., 2008). Thus we currently hypothesise, based upon metagenomic data, that within the CAP approximately 10% of total DNA is archaeal and dominated by members of the Methanosarcinaceae which are versatile organisms producing methane from substrates such as, H2–CO2, methanol, methylamines, acetate, and CO via hydrogenotrophic or aceticlastic pathways (Bryant and Boone, 1987). However, this uncertainty as to whether anaerobic

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digestion is driven by aceticlastic or hydrogenotrophic pathways in CAPs has important management implications and needs to be further resolved in order to maximise the conversion of waste to biogas. In summary, understanding microbial community dynamics and their impact on the functional potential of microbial systems has become increasingly important as microbial ecologists and technologists strive to manipulate microbial systems to deliver ecosystem services such as waste conversion, carbon sequestration or improvements in crop yield. The introduction of low cost, scalable and rapid NGS technologies such as the PGM platform provide small to medium laboratories with the high throughput capabilities which allow a fundamental basis to these studies, as well as affording highly replicated and longitudinal studies in a cost effective way. All these factors will provide substantial increases in primary sequence generation and will feed into the ultimate goal of the description of microbial diversity and functionality within the myriad of natural and engineered microbial ecosystems. Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.mimet.2012.07.008. Acknowledgements This work was funded by grants from Australia Pork Ltd and the Australian Grains Research and Development Corporation (GRDC), from UWA and the Vice Chancellor's Discretionary Fund and through the core science budget of CEH to support the sabbatical visit of ASW to UWA. We thank Jack Gilbert for the advice and sequences relating to the Golay Barcodes, Kelly Ewen-White for the discussions regarding the PGM and Charles Morgan for the provision of the PGM through a charitable donation. References Amann, R.I., Ludwig, W., Schleifer, K.H., 1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59, 143–169. Barns, S.M., Fundyga, R.E., Jeffries, M.W., Pace, N.R., 1994. Remarkable archaeal diversity detected in a Yellowstone National Park hot spring environment. Proc. Natl. Acad. Sci. U. S. A. 91, 1609–1613. Bartram, A.K., Lynch, M.D.J., Stearns, J.C., Moreno-Hagelsieb, G., Neufeld, J.D., 2011. Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end Illumina reads. Appl. Environ. Microbiol. 77, 3846–3852. Bowers, R.M., Sullivan, A.P., Costello, E.K., Collett, J.L., Knight, R., Fierer, N., 2011. Sources of bacteria in outdoor air across cities in the midwestern United States. Appl. Environ. Microbiol. 77, 6350–6356. Bryant, M.P., Boone, D.R., 1987. Amended description of strain MST(DSM 800T), the type strain of Methanosarcina barkeri. Int. J. Syst. Bacteriol. 37, 169–170. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Gonzalez, A., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. Caporaso, J., Paszkiewicz, K., Field, D., Knight, R., Gilbert, J.A., 2011. The Western English Channel contains a persistent microbial seed bank. ISME J. 6, 1089–1093. Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S.M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J.A., Smith, G., Knight, R., 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. http://dx.doi.org/10.1038/ismej.2012.8 (e-pub ahead of print 8th March). Cook, K.L., Rothrock, M.J., Lovanh, N., Sorrell, J.K., Loughrin, J.H., 2010. Spatial and temporal changes in the microbial community in an anaerobic swine waste treatment lagoon. Anaerobe 16, 74–82. Erlich, Y., Mitra, P.P., delaBastide, M., McCombie, W.R., Hannon, G.J., 2008. Alta-cyclic: a self-optimizing base caller for next-generation sequencing. Nat. Methods 5, 679–682. Fierer, N., Lauber, C., Ramirez, K., Zaneveld, J., Bradford, M., Knight, R., 2011. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017. Gilbert, J.A., Meyer, F., Antonoploulos, D., Balaji, P., Brown, C.T., Desai, N., Eisen, J.A., Evers, D., Field, D., Feng, W., Huson, D., Jansson, J., Knight, R., Knight, J., Kolker, E., Konstantindis, K., Kostka, J., Kyrpides, N., Mackelprang, R., McHardy, A., Quince, C., Raes, J., Sczyrba, A., Shade, A., Stevens, R., 2010. Meeting report. The terabase metagenomics workshop and the vision of an earth microbiome project. stand. Genomics Sci. 3, 243–248. Glenn, T.C., 2011. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759–769.

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