Soil Biology & Biochemistry 42 (2010) 739e747
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A phylogenetic microarray targeting 16S rRNA genes from the bacterial division Acidobacteria reveals a lineage-specific distribution in a soil clay fraction Mark R. Liles a, Ozgur Turkmen b, Brian F. Manske c, Mingzi Zhang c, Jean-Marie Rouillard d, Isabelle George e, Teri Balser f, Nedret Billor b, Robert M. Goodman g, * a
Department of Biological Sciences, Auburn University, Auburn, AL, USA Department of Mathematics and Statistics, Auburn University, Auburn, AL, USA Department of Plant Pathology, University of Wisconsin, Madison, WI, USA d Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA e Unité de Génie Biologique, Université Catholique de Louvain, Louvain-la-Neuve, Belgium f Department of Soil Sciences, University of Wisconsin, Madison, WI, USA g School of Environmental and Biological Sciences, Rutgers, The State University of New Jersey, 88 Lipman Drive, Suite 104, New Brunswick, NJ 08901, USA b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 October 2009 Received in revised form 8 January 2010 Accepted 14 January 2010 Available online 4 February 2010
We designed an oligonucleotide microarray using probe sequences based upon a phylogenetic analysis of 16S rRNA genes recovered from members of the bacterial division Acidobacteria. A total of 42,194 oligonucleotide probes targeting members of the Acidobacteria division at multiple phylogenetic levels were included on a high-density microarray. Positive control hybridizations revealed a linear relationship between hybridization signal and template concentration, and a substantial decrease in non-specific hybridization was achieved through the addition of 2.5 M betaine to the hybridization buffer. A mean hybridization signal value was calculated for each Acidobacteria lineage, with the resultant lineagespecific hybridization data revealing strong predictive value for the positive control hybridizations. The Acidobacteria phylochip was then used to evaluate Acidobacteria rRNA genes from a Wisconsin soil and within a soil clay fraction. The Acidobacteria hybridization profile revealed the predominance of Acidobacteria subdivisions four and six, and also suggested a decrease in the abundance of subdivision six relative to subdivision four in the soil clay fraction. The change in relative abundance of these subdivisions in a soil clay fraction was supported by data from quantitative PCR. These results support the utility of a phylogenetic microarray in revealing changes in microbial population-level distributions in a complex soil microbial assemblage. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Acidobacteria 16S rRNA Microarray Phylogeny Clay Phylochip
1. Introduction The vast majority of prokaryotes live within oligotrophic natural environments and are poorly represented in culture collections (Amann et al., 1995; Torsvik et al., 1990, 1994, 1996). This is especially true of soil microorganisms, which have contributed greatly to our arsenal of antimicrobial agents, yet every census of soil microorganisms to date has revealed only fragmentary evidence of the extant phylogenetic and metabolic diversity present in any soil. Efforts to understand the distribution and ecological roles of environmental microorganisms have been aided by the molecular suite of tools now available, particularly those based on analysis of the small subunit ribosomal RNA gene (16S rRNA). Polymerase
* Corresponding author. þ1 732 932 9000x500. E-mail address:
[email protected] (R.M. Goodman). 0038-0717/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2010.01.007
chain reaction (PCR) amplification of 16S rRNA genes from natural environments reveals many 16S rRNA gene sequences that are highly divergent from known cultured phyla (Woese, 1987; Hugenholtz et al., 1998a,b; Meier et al., 1999; Dojka et al., 2000; Wilson et al., 2002). Many of the highly divergent 16S rRNA gene sequences together comprise entirely new monophyletic prokaryotic lineages, forming newly recognized divisions (Barns et al., 1994; Head et al., 1998; Hugenholtz et al., 1998a,b; Pace et al., 1986; Ward et al., 1990). Of the bacterial divisions revealed primarily by rRNA gene sequence data, the division Acidobacteria is ubiquitous in soils and sediments (Liesack et al., 1994; Kuske et al., 1997; Barns et al., 1999). All of the cultured Acidobacteria isolates to date fall into four of the proposed subdivisions (Kishimoto et al., 1991; Ludwig et al., 1997; Hugenholtz et al., 1998a,b; Barns et al., 1999; Janssen et al., 2002), the number of which has recently been expanded to include up to 26 subdivisions (Zimmermann et al., 2005; Barns et al., 2007). A phylogenetic analysis of 16S rRNA gene sequences
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recovered from a soil at the West Madison Agricultural Research Station (WMARS) revealed that 25% of the rRNA genes were affiliated with the Acidobacteria division (Liles et al., 2003). Phylogenetic microarrays, or “phylochips”, have been used to discriminate rapidly between diverse 16S rRNA genes present within cultured or environmental microorganisms (Wilson et al., 2002; Brodie et al., 2006; Palmer et al., 2006; Huyghe et al., 2008). Compared to the labor- and resource-intensive efforts to clone and sequence a representative number of clones from a 16S rRNA gene clone library, phylogenetic microarrays can provide an efficient readout of the phylogenetic diversity present in an environmental sample. Furthermore, a hierarchical design allows probing for microbial taxa at different phylogenetic levels (Huyghe et al., 2008), giving information on the presence or absence of the branches and the twigs on the tree of life. In the present study, the division Acidobacteria was targeted by a phylogenetic microarray approach, with oligonucleotide probes targeting multiple phylogenetic levels. A series of positive control hybridizations were used to validate the phylogenetic microarray design and hybridization conditions, followed by experiments to determine the distribution of Acidobacteria taxa in a soil sample and a clay fraction from this same soil sample. Results from the Acidobacteria phylogenetic microarray were then tested by an independent molecular analysis to validate microarray hybridization results. 2. Materials and methods 2.1. Soil collection Soil cores were collected from an undisturbed site at the West Madison Agricultural Research Station (WMARS) (Bintrim et al., 1997; Rondon et al., 2000). The ecosystem is a turfgrass understory without organic amendments. The soil type is a Plano silt loam containing 61% sand, 23% silt, and 16% clay, with 1.7% organic matter (pH 7.0). The top 10 cm of soil were sampled and sieving was used to remove roots and debris. Genomic DNA was isolated immediately after soil collection. The remainder of the soil sample was frozen at 80 C. 2.2. DNA isolation from soil or Escherichia coli cultures A bead-beating method (Bio101, Inc., La Jolla, CA) was used to isolate genomic DNA from soil microorganisms. The genomic DNA isolated via a Bio101 kit is generally less than 20 kb in size, yet the harsh lysis conditions ensure that the genomic DNA is broadly representative of the soil microbial community (Burgmann et al., 2001). Samples were stored at 20 C until further analysis. A set of positive control Acidobacteria rRNA genes was available from a previous study, with recombinant clones containing Acidobacteria rRNA operons and associated genes cloned within a bacterial artificial chromosome (BAC) vector (Liles et al., 2003). BAC DNA was isolated from E. coli cultures grown in Luria Broth (BD Diagnostic Systems, Sparks, MD) overnight while shaking at 37 C, using a Large-Construct DNA Isolation kit (Qiagen, Inc., Valencia, CA). The use of an Acidobacteria-specific primer set prevented E. coli 16S rRNA gene contamination of the amplicons. 2.3. PCR amplification of rRNA genes An Acidobacteria 16S rRNA gene-specific primer (31F) was used to amplify 16S rRNA genes from soil genomic DNA or positive control 16S rRNA gene clones by PCR (Barns et al., 1999). It should be noted that although this division-level primer is not predicted to PCR amplify 16S rRNA genes from all of the extant Acidobacteria subdivisions (Barns et al., 2007), the Acidobacteria subdivisions present
within WMARS soil were identified using a universal bacterial primer set (Liles et al., 2003) and each of the Acidobacteria subdivisions identified in WMARS may be PCR amplified using the Acidobacteria division-level primer 31F. Amplicons were produced using approximately 100 ng DNA template, 1 unit Taq polymerase (Promega, Madison, WI), 1 Taq polymerase reaction buffer, 200 mM dNTPs, and 200 nM of each of the primers 31F (50 -GATTCTGAGCCAAGGATC, Acidobacteria-division specific)(Barns et al., 1999) and 1492R (50 -ACGGYTACCTTGTTACGACTT, universal Bacteria domain) (Medlin et al., 1988) in 50-ml. Reactions to be used for microarray hybridization directly incorporated the Cy3-dCTP dye (GE Healthcare, Piscataway, NJ) into the PCR product using a final concentration of 40 mM Cy3-dCTP (20% of total dCTP) according to manufacturer's instructions. The reaction was performed with 3 min denaturation at 95 C, 30 cycles of 95 C for 1 min, 55 C annealing for 90 s, 72 C extension for 150 s, followed by 7 min extension at 72 C. All reactions were carried out in a Robocycler 96 (Stratagene, La Jolla, CA) with 50 ml mineral oil added to each tube. Reactions were analyzed by agarose gel electrophoresis to confirm production of a single (heterogeneous in the case of soil genomic DNA template) amplicon. The resulting PCR products were fragmented to an approximate average size of 200 bp using diluted DNAse I (final concentration 0.004 U/ml) for 30 min at 37 C. Consistent DNA fragmentation was monitored by agarose gel electrophoresis. To remove unincorporated Cy3-dCTP, the fragmented PCR products were purified and concentrated over Centricon 3000 NMWL centrifuge concentrators (Millipore, Inc., Billerica, MA). The concentration of fragmented, labeled PCR product was determined using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). 2.4. Phylogenetic analysis Acidobacteria 16S rRNA gene sequences were aligned to a dataset of 6883 bacterial sequences (courtesy of Dr. Phillip Hugenholtz, http://rdp.cme.msu.edu/html/alignments.html) using the ARB software package (http://www.arb-home.de/) and refined manually to remove regions of ambiguous homology. Alignments used for phylogenetic analysis were minimized by the Lane mask (Lane et al., 1985) for bacterial data or an Acidobacteria filter (phylum specific 50% filter by base frequency) prepared in ARB. Phylogenetic trees for near full-length sequences (>1400 nt) were inferred within the ARB package using evolutionary distance (neighborjoining algorithms with Felsenstein correction) and the PHYLIP program for maximum parsimony (Felsenstein, 1993). Partial sequences (1300 bp) and the other of full and partial sequences (>500 bp). Probes were designed for all clades supported by bootstrap values >¼ 85% (parsimony), supporting the presence of 10 monophyletic Acidobacteria subdivisions. Values used in the ARB probe design function were as follows: Length of Output ¼ 100; Max. non-group hits ¼ 0; Max. hairpin bonds ¼ 4;
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Min. group hits (%) ¼ 75; Length of probe ¼ 18, 19, and 20; Temperature ¼ 30e80; G þ C content (%) ¼ 50e80. Reverse complements were generated and added to the probe list, since each strand of a rRNA gene amplicon is capable of hybridization to the microarray. All probes were assembled into a list with associated target clade, sequence, probe length, predicted Tm, G þ C content and corresponding E. coli alignment position. A set of 70 additional oligonucleotide probes targeting various divisions of Bacteria were obtained from probeBase (http://www. microbial-ecology.net/probebase/) and were included in the oligonucleotide probes on the microarray. All oligonucleotide probes used in this study, as well as their predicted phylogenetic specificity, E. coli relative position, length in base pairs, % G þ C content, predicted Tm, and all hybridization signals for each phylogenetic group are deposited within the Gene Expression Omnibus (GEO) as accession GSE18711 (http://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?token¼plcldieqayqccju&acc¼GSE18711). 2.6. Spiked-in oligonucleotides In initial hybridization experiments, the 70 probe sequences specific to non-Acidobacteria 16S rRNA genes were tested for hybridization to an Acidobacteria rRNA gene PCR product. Three probes (DHP1006, 50 -CCTCTCGATCTCTCTCAAGT (McSweeney et al., 1993); GSB532, 50 -TGCCACCCCTGTATC (Tuschak et al., 1999), LGC354A, 50 -TGGAAGATTCCCTACTGC (Meier et al., 1999)) that showed the least background hybridization were chosen for use as oligonucleotide probes to include in each hybridization to help control for microarray-to-microarray variability. For each experiment, Cy3-labeled oligonucleotides complementary to these three probes were added to the hybridization reaction, and hybridization signals for each control probe were averaged among replicates. 2.7. Microarray design and construction A custom microarray containing a total of 182,318 total features was synthesized onto a glass slide using maskless array synthesis (MAS) (Roche Nimblegen, Inc., Madison, WI), with three replicates of each oligonucleotide probe. Probe lengths ranged from 15 to 20 bp, with all Acidobacteria-specific oligonucleotides in the 18e20 bp size range. Slides were kept desiccated at room temperature prior to hybridization. 2.8. Microarray hybridization Each hybridization reaction had a total volume of 20 ml which contained bovine serum albumin (0.2 mg/ml, Sigma Chemical Co.), sheared salmon sperm DNA (0.14 mg/ml, Invitrogen, Inc.), 1 Nimblegen hybridization buffer (Roche Nimblegen, Inc.), spiked-in oligos (10 nM each, see sequences above), and 100 ng fragmented and denatured PCR product. Hybridizations were conducted at 45 C for 16 h in a Nimblegen 12 Bay Hybridization System (Roche Nimblegen, Inc.). Most reactions (except for first control hybridizations) contained 2.5 M betaine to enhance hybridization stringency. Microarrays were washed 3 times with wash solutions I, II, and III (Roche Nimblegen, Inc.) before being dried using N2 gas. 2.9. Microarray scanning Each hybridized microarray was scanned using an Axon4000B scanner (Molecular Devices, Sunnyvale, CA) at 10 mm resolution. GenePixÒ Pro 6.0 software was used for the analysis and processing of signal strength from each scanned microarray. Overall signal strength for each microarray was adjusted to prevent inclusion of
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saturated signals. Signal intensity for each feature was determined and exported into a tab-delimited file for subsequent analysis.
2.10. Microarray data analysis Data were imported into an Excel spreadsheet and sorted by phylogenetic group. Data were then normalized in relation to a mean hybridization signal observed from the set of Acidobacteria division-level probes. Since this set of Acidobacteria division-level probes was universal for every hybridized PCR amplicon, and the signal strength was consistently strong for each of these divisionlevel probes, microarray normalization relative to the Acidobacteria division-level probes provided the best array-to-array consistency, compared to normalization using spiked-in oligonucleotide probes. A mean hybridization signal for each phylogenetic group relative to the Acidobacteria division-level probes was determined for every experiment, and data were analyzed for statistical significance as described below.
2.11. Statistical analysis of oligonucleotide probe thermodynamic characteristics Several outlier detection methods were used to identify probes with extreme values of %G þ C content, DG62, Tm, and/or DG62-fold to eliminate probes that would not be predicted to perform well in the hybridization reaction. DG62, Tm and DG62-fold were predicted using the hybrid-min and hybid-ss-min software from the UNAfold package (Markham and Zuker, 2005, 2008) using a temperature of 62 C (hybridization temperature) and sodium and DNA concentrations of 1 M and 1 mM respectively. Since there was a sufficient number of features on the microarray to include all oligonucleotide probes regardless of predicted thermodynamic characteristics, all probe sequences identified by ARB were included on the microarray and outlier sequences were analyzed and removed posthybridization. This permitted a comparison between the dataset of probe hybridizations with and without removal of outlier probes. The Mahalanobis distance based outlier detection methods such as the minimum covariance determinant (MCD) (Rousseeuw and van Driessen, 1999) and BACON (Billor et al., 2000) were used for 42,142 observations to identify “bad” probes. Any observation that exceeded c2 based cutoff value was identified as a “bad” probe. The rest of the statistical analyses were carried out on the clean data after eliminating w7% “bad” probes with in silico predicted poor hybridization characteristics.
2.12. Statistical analysis of the effect of betaine on hybridization stringency A series of positive control hybridizations was conducted first to identify hybridization conditions for optimal resolution between phylogenetic groups. An experiment was conducted with positive control Acidobacteria subdivision 4 rRNA clone 220H (Liles et al., 2003) with and without the addition of 2.5 M betaine in the hybridization buffer, using the exact same PCR amplicon for hybridization to the microarrays with and without betaine addition. The mean hybridization signal for all true-positive phylogenetic groups (i.e., corresponding to the predicted phylogeny from Acidobacteria subdivision 4 containing sequence 220H) were compared to all other phylogenetic groups based on a Tukey's Studentized Range (HSD) test.
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2.13. Statistical analysis of phylogenetic microarray hybridization experiments Within the Acidobacteria division-level phylogeny, there were 76 non-overlapping lineages (i.e., terminal branches) that had over 75% bootstrap support. For every microarray hybridization, pairwise tests between each Acidobacteria phylogenetic lineage were performed to compare each lineage to the others. The TukeyeKramer method was used to compare all pairwise differences in phylogenetic group mean hybridization signals. A false discovery rate was also utilized to control the expected proportion of incorrectly rejected null hypotheses (type I errors) in a list of rejected hypotheses. 2.14. Soil fractionation Soil from WMARS was wet sieved through a fine mesh, and fractionated into sand, silt, and clay fractions by differential centrifugation (Poll et al., 2003). The sand fraction was washed repeatedly to remove any other soil particles. 2.15. Quantitative PCR Acidobacteria subdivision 4 (50 -GGCGAAAGCCTGACCCAGCA, at equivalent E. coli 16S rRNA position bp 376) and subdivision 6-specific (50 -ACGCGGCGTTGCGTCGTC, at equivalent E. coli 16S rRNA position bp 387) oligonucleotide primers were designed to PCR amplify these subdivisions from soil community genomic DNA, in conjunction with an Acidobacteria division-level primer (e.g., 31F) to provide sufficient phylogenetic specificity. In the case of the subdivision 4-specific primer, this is also predicted in silico (Ribosomal Database Project, http://rdp.cme.msu.edu/) to amplify rRNA genes from Acidobacteria subdivision 21, but using the forward primer 31F should restrict this primer pair to amplify Acidobacteria subdivision 4 rRNA templates. These subdivision-specific primer pairs produced amplicons of equivalent size (345 bp or 356 bp, respectively) and with equivalent thermodynamics based on amplification reactions using positive control templates (PCR amplified rRNA gene clones) at known concentrations. In each set of reactions a serial dilution of positive control templates was included to provide quantitative comparisons between samples (limit of detection 0.01 ng rRNA gene template DNA for both subdivision 4 and 6 primer sets) and establish amplification efficiency. Metagenomic DNA isolated from soil samples or soil fractions was used as a template in a quantitative PCR with 100 ng of soil genomic DNA in a 50 ml reaction including 1 iQ SYBR Gren Supermix (BioRad Laboratories, Hercules, CA) containing 100 nM of each subdivision-specific primer. The quantitative comparison between Acidobacteria subdivisions 4 and 6 in different soil fractions was based upon the calibrated curve of DNA (cloned 16S rRNA genes) from the respective Acidobacteria subdivisions used as template in positive control reactions (n ¼ 4 for all reactions). 3. Results 3.1. Phylogenetic analysis of Acidobacteria rRNA genes Every Acidobacteria 16S rRNA gene sequence available in the GenBank nr/nt database was imported into the ARB phylogenetic analysis software package. Of the 73 Acidobacteria sequences obtained from a phylogenetic analysis of WMARS soil, 58 affiliated with Acidobacteria subdivision 6 (Hugenholtz et al., 1998a,b), which was the dominant Acidobacteria subdivision in this soil in multiple phylogenetic surveys (Liles et al., 2003). No chimeric rRNA gene sequences were identified from this study. A total of 10 Acidobacteria subdivisions were supported by bootstrap analysis, with
subdivisions 9 and 10 with limited numbers of affiliated sequences (note that the uranium-contaminated soil sequences which indicated additional Acidobacteria subdivisions (Barns et al., 2007) had not yet been included within this database and were also not found within WMARS soil). Using the probe-finding functions of ARB, the Acidobacteria clades with at least 75% bootstrap support were chosen to identify 18-, 19-, and 20-bp oligonucleotide probes that would be predicted to hybridize only with the specific phylogenetic lineage. In total, 76 Acidobacteria lineages were supported by bootstrap support, and 42,194 oligonucleotide probes were chosen on the basis of phylogenetic specificity. 3.2. Effect of betaine on non-specific hybridization signal The effect of adding 2.5 M betaine to the microarray hybridization buffer in increasing hybridization stringency was assessed using a positive control Acidobacteria 16S rRNA gene template (i.e., clone P220H). First, a range of concentrations of the positive control template was hybridized to the Acidobacteria phylochip, using 10 ng, 50 ng, 100 ng, or 250 ng of PCR amplicon, respectively, without including betaine in the hybridization buffer. These experiments indicated that 100 ng of rRNA gene amplicon provided the best signal to noise from the microarray hybridization (data not shown). To test the effect of adding betaine to the hybridization buffer on the degree of background hybridization, 100 ng of a single, labeled rRNA gene amplicon (i.e., clone 220H) was hybridized under two conditions, with and without 2.5 M betaine, with all other hybridization conditions remaining identical. The subset of probes predicted to be positive had a mean normalized (relative to the universal Acidobacteria probe set) hybridization signal of 80% with betaine (Fig. 1A), and 78% without added betaine (Fig. 1B), a difference that was not statistically significant. However, the subset of probes predicted to be negative had a mean normalized hybridization signal of 13% in the absence of betaine (Fig. 1B), which dropped to only 5% when 2.5 M betaine was added to the hybridization buffer, a reduction of 62% of the non-specific background hybridization (Fig. 1A). Using nonparametric hypothesis testing procedures we tested if expected-negative probes with and without the addition of betaine would cause the same background hybridization, and concluded that there is strong statistical evidence that the expected-negative probes with and without the addition of betaine are different (P < 0.0001) at a ¼ 0.05 significance level. As a result, the inclusion of betaine in the hybridization buffer results in much lower background hybridization for all expected-negative probes. 3.3. Positive control phylochip hybridizations A set of positive control hybridizations was conducted using single, cloned Acidobacteria 16S rRNA genes under the higher stringency hybridization conditions achieved by including 2.5 M betaine within the hybridization buffer. Each of the “expected positive” phylogenetic groups produced a hybridization signal (mean for all probe hybridization signals per group) that was significantly greater than the mean value for all “expected-negative” with each of the positive control rRNAs, indicating the presence of an Acidobacteria taxa in subdivision 5 (clone P17F) (Fig. 2A) or an Acidobacteria taxa in subdivision 6 (clone P147G) (Fig. 2B). Other positive control 16S rRNA gene amplicons from clones 16H1 (subdivision 4) and clone 85E8 (subdivision 6) also revealed levels of hybridization signal for all expected-positive lineages that were greater than the mean hybridization signal for the microarray (data not shown); specifically, for positive control amplicons from clones 16H1 and 85E8 the mean normalized hybridization signal for all
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Fig. 1. Betaine improves hybridization stringency. Normalized hybridization signal from an Acidobacteria phylogenetic microarray using as template a single cloned 16S rRNA gene (clone P220H, subdivision 4) in the presence (A) or the absence (B) of 2.5 M betaine in the hybridization buffer. The mean hybridization signal from each phylogenetic probe (in triplicate) was grouped with every other probe from the same phylogenetic level, and the mean hybridization signal for each phylogenetic group was then normalized by comparison to the set of universal Acidobacteria probes. Each phylogenetic group is represented by a separate bar on the X-axis, and ten of the Acidobacteria subdivisions are indicated below their respective clades. The bar in blue indicates the universal probe set that were used to normalize the microarray data, and the bars in red represent the expected-positive phylogenetic groups for this hybridization.
Fig. 2. Control 16S rRNA gene microarray hybridizations yield expected phylogeny. Normalized hybridization signal from a phylogenetic microarray using as template a single cloned 16S rRNA gene from (A) Acidobacteria subdivision 5 (clone P17F) or from (B) Acidobacteria subdivision 6 (clone P147G). The hybridization included 2.5 M betaine to achieve higher stringency hybridization, and all hybridization signals were normalized by comparison to universal probes. The bar in blue indicates the universal probe set that were used to normalize the microarray data, and the bars in red represent the expected-positive phylogenetic groups for each hybridization.
expected-negative features was 0.098 and 0.093, and the mean normalized hybridization signal for all expected-positive features was 1.14 and 0.54, respectively. As the template concentration was identical in all hybridizations, the differences in the normalized hybridization signal for expected-positive features between different rRNA gene sequences (e.g., clone 16H1 versus clone 85E8) is a consequence of different probe hybridization kinetics. As expected with large numbers of distinct oligonucleotide probes, the absolute signal intensity is not a quantitative indicator of rRNA template concentration, rather it is the change in the normalized signal strength of specific phylogenetic groups relative to other phylogenetic groups from array-to-array that is predicted to reveal changes in the relative abundance of bacterial taxa. In some cases, an expected-negative phylogenetic group also produced a significant hybridization signal; however, upon checking the individual probes that produced higher signals, these were always observed to correspond to probe sequences with only a single base pair mismatch with the positive control rRNA. In other words, the vast majority of probes that were expected to be negative in the control hybridization reactions did not give a significant hybridization signal, and only the probes that had a sequence with a single base
3.4. WMARS soil phylochip hybridizations
pair mismatch from the positive control rRNA sequence were capable of a significant degree of mismatch hybridization.
The soil sample from which the positive control rRNAs were derived was used as a template to produce a soil Acidobacteria PCR amplicon. Since a phylogenetic analysis had been previously performed on this soil sample (Liles et al., 2003) to some extent the diversity of Acidobacteria subdivisions present in this soil sample could be predicted and compared to the resultant phylogenetic results from microarray hybridization. Furthermore, the Acidobacteria division-level probe that was used, although not capable of encompassing all described Acidobacteria subdivisions, did however completely cover the Acidobacteria taxa known to predominate within this specific soil sample. To encompass an exhaustive sampling of Acidobacteria taxa within a rRNA amplicon derived from a previously uncharacterized environmental sample, it would simply require using multiple Acidobacteria division-level primers (or universal Bacteria primers, in the case of a domain Bacteria phylochip). Two replicate soil metagenomic DNA extractions had been performed on this soil sample, and were used independently for two separate PCR amplifications of Acidobacteria rRNA genes and subsequent microarray hybridizations. The hybridization signals
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other subdivision 2-specific clade gave a strong hybridization signal. The presence of subdivisions 4 and 6 in WMARS soil was supported by the hierarchical lineages indicated by probe hybridizations, and by the strength of the hybridization signals for some lineages in subdivisions 4 and 6, which exceeded the mean value of all probes by more than 3-fold in some cases. Some of the positive control rRNAs used to validate the microarray hybridization conditions (i.e., clone P16H in subdivision 4, and clone P147G in subdivision 6) were also in these same respective Acidobacteria lineages, again reflecting the high relative abundance of these bacterial taxa in this WMARS soil. No significant difference was observed between the replicate soil sample hybridizations, as the signal strength for both subdivision 4 and 6 lineages were strongly supported. 3.5. WMARS soil clay fraction phylochip hybridizations
Fig. 3. Two distinct Acidobacteria lineages observed in WMARS soil and a soil clay fraction. Normalized hybridization signal from a phylogenetic microarray using as template metagenomic DNA extracted from (A) a WMARS soil sample, or (B) a clay fraction of this same WMARS soil sample. Each phylogenetic group was normalized by comparison with the set of universal probes, and the mean value of all (non-universal) probes is indicated by a dotted line. The phylogenetic lineage from subdivision 4 (4.1.2.2.2.2.2.2.1) that exceeded the mean probe value is indicated in red, whereas the subdivision 6 lineage (6.2.3.1.1.2.1) is indicated in yellow. Other phylogenetic groups with hybridization signals exceeding the mean (non-universal) probe signal are indicated in white. Note that of the phylogenetic groups indicated in white, other phylogenetic clades within the same subdivision do not show significant strength of signal, resulting in less support for their presence within the soil sample.
resulting from the soil Acidobacteria amplicons were highly similar to each other (data not shown), had significantly greater background hybridizations signals compared to control hybridizations, and both indicated the high relative abundance of Acidobacteria taxa in subdivisions 4 and 6, respectively (Fig. 3A). An Acidobacteria lineage was determined to be present within the soil sample if there was a strong hybridization signal from a particular phylogenetic lineage beginning with the subdivision level. A strong hybridization signal was defined as a normalized, mean hybridization signal for a specific phylogenetic group that is greater than the mean hybridization signal for all probes (indicated on Fig. 3 as a dashed blue line). The WMARS soil microarray results indicated that two distinct Acidobacteria lineages, in subdivision 4 (lineage 4.1.2.2.2.2.2.2.1) and subdivision 6 (lineage 6.2.3.1.1.2.1), were present in WMARS soil, with strong hybridization signals observed at multiple phylogenetic levels for each lineage (Fig. 3A). Other phylogenetic groups (i.e., lineages 1.1, 1.3, 1.25, 3.1, 3.12, and 5.3 indicated in white on Fig. 3A) did exceed the minimum cutoff value for a strong hybridization signal, but were rejected since the subdivision-level probes did not support their presence (i.e., subdivisions 1, 3, and 5 signals were insufficiently strong, respectively). Subdivision 2 probes did give a mean value greater than the cutoff level for significance, yet no
The microarray hybridization signals observed when metagenomic DNA isolated from the WMARS soil clay fraction was used as a template closely resembled the hybridization pattern observed from the whole soil sample. Both of the lineages that were strongly supported to be present in the WMARS soil (i.e., 4.1.2.2.2.2.2.2.1 and 6.2.3.1.1.2.1) were also observed with the two replicate clay fractions (Fig. 3B). The other similarity to the entire soil hybridization included a greater degree of non-specific hybridization as compared to positive control hybridizations. However, the magnitude of the signals from the lineages in subdivisions 4 and 6, although both strongly supported, were observed to vary relative to one another by comparison with the whole soil sample. Specifically, the normalized signal strength of lineage 4.1.2.2.2.2.2.2.1 was consistently stronger than lineage 6.2.3.1.1.2.1 in the whole soil sample relative to the clay fraction in both of the replicate samples. The punctured normal distribution (Lai et al., 2004) which is the special case of a truncated normal distribution was used to test if an analysis of the Acidobacteria community within the clay fraction of this soil suggested a decrease in the abundance of subdivision 6 relative to subdivision 4. We concluded that the mean ratio of the subdivision 4 lineage relative to the subdivision 6 lineage for clay is greater than this same ratio in bulk soil, supporting the indication of a decrease in the abundance of the subdivision 6 lineage relative to the subdivision 4 lineage. There was a consistent difference in the ratio of subdivision 4 and 6 in the replicate microarray hybridizations, with the ratio of subdivision 4 to subdivision 6 lineages (using a mean value for all respective sub-lineages) being 86.3% and 87.9% for the two replicate soil samples, and 108.3% and 106.6% for the two replicate clay fraction samples. 3.6. Quantitative PCR of Acidobacteria subdivisions in soil fractions Subdivision 4- and subdivision 6-specific primer sets were calibrated using known concentrations of cloned and PCR amplified 16S rRNA genes, from the respective subdivisions, for quantitative assessment of these two subdivisions in soil samples and soil fractions. Both of the subdivision-specific primer sets yielded PCR products with at least 0.01 ng of added 16S rRNA gene template and gave comparable yield of amplicons at equal template concentrations. The subdivision-specific quantitative PCR data from whole soil and soil fractions revealed a significantly higher amount of subdivision 6 template in all samples, relative to subdivision 4, and that in the clay fraction of WMARS soil the subdivision 6 template was at its lowest concentration relative to subdivision 4 taxa compared to all other soil samples and fractions (P < 0.01 by Student's t-test). Therefore, the ratio of subdivision 4 template levels relative to subdivision 6 template levels were at their very highest within the clay fraction compared to whole soil or any other soil fraction (Fig. 4).
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Fig. 4. Quantitative PCR analysis indicates an increased ratio of Acidobacteria subdivision 4 relative to subdivision 6 within the clay fraction of WMARS soil. Subdivisionspecific primer sets were used to quantitatively determine the amount of subdivision 4 and subdivision 6-specific templates, respectively, relative to total metagenomic DNA extracted from WMARS soil and different soil fractions. The ratio of subdivision 4 template DNA compared to subdivision 6 template DNA was determined for each soil sample or soil fraction, with a statistically significant increase in this ratio observed in the soil clay fraction relative to all other samples (P < 0.01 by Student's t-test). Letters indicate statistical groupings and error bars indicate standard deviations.
4. Discussion The massively parallel nature of microarray hybridizations, combined with a large database of 16S rRNA gene sequences, enables rapid identification of bacterial phylotypes present within pure cultures or environmental samples. This ability is especially useful when the bacterial taxa may be identified by a phylogenetic signature, yet be recalcitrant to cultivation. In our study the bacterial division Acidobacteria was chosen as the focus of a phylogenetic microarray to target its diverse taxa that had been shown to be present at a high relative abundance in 16S rRNA gene clone libraries in many soils, and had been extensively studied from a soil in Madison, WI (WMARS soil). Due to the difficulties inherent in conducting a molecular phylogenetic census from a soil sample, and the potential advantages that would accrue from being able to apply a phylogenetic microarray approach to previously uncharacterized soil microbial assemblages, this study was initiated to test specific hypotheses regarding the predictive power of a phylogenetic microarray in reflecting the bacterial taxa present within a soil microbial assemblage, using a well defined (as best as was available) soil sample for this analysis. The first hypothesis tested in this study was that the use of a hierarchical design of oligonucleotide probes, targeting bacterial taxa at different phylogenetic levels, could be used to identify specific lineages of the Acidobacteria division. In many studies using phylogenetic microarrays, only previously characterized oligonucleotide probes were included that had been validated against a cultured bacterial target. In our study, due to the lack of cultured isolates for many clades of the division Acidobacteria, this was not feasible. Furthermore, the very large number of unique oligonucleotide probes identified from an ARB database phylogenetic analysis of the Acidobacteria division precluded individual assessment of each probe prior to its inclusion on the microarray. Rather, three different approaches were taken to validate the oligonucleotide probes on the phylogenetic microarray: 1) A set of control hybridizations with 16S rRNA genes of known sequence were used to evaluate the degree of expected-positive probe hybridization signal strength, relative to expected-negative signals from known negative probes (for their respective control rRNA gene sequences). From evaluating the positive control hybridizations using
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increasing concentrations of betaine, a higher degree of hybridization stringency was achieved which was then used in all subsequent experiments; 2) Every oligonucleotide probe included on the microarray was evaluated using in silico predictions of thermodynamic properties (i.e., % G þ C content, DG, Tm, and DGfold), and probe outliers were identified. A comparison of results from each of the positive control and soil sample experiments, with and without exclusion of the outlier probes, did not yield any statistically different results. Likely, this is a function of the large number of separate probes included on the microarray that target each phylogenetic clade, so that any small number of outlier probes (e.g., resulting in self-complementarity and/or decreased binding to rRNA gene targets) did not significantly affect the overall mean hybridization results; 3) The presence of a particular phylogenetic lineage was strongly supported by the presence of a strong hybridization signal at multiple phylogenetic levels. For example, from the soil hybridization results the lineage 6.2.3.1.1.2.1 was evident from both of the replicate microarray hybridizations. In this case, the probes at phylogenetic levels 6, 6.2, 6.2.3, 6.2.3.1, 6.2.3.1.1, 6.2.3.1.1.2, and 6.2.3.1.1.2.1 each gave a mean hybridization signal significantly greater than was observed for the mean value for all probes included on the microarray; thus, the cumulative data for the presence of lineage 6.2.3.1.1.2.1 within the WMARS soil was derived from multiple phylogenetic levels, as well as the previous phylogenetic analysis of WMARS soil that had demonstrated the abundance of this subdivision 6 clade. In conclusion, the data support the utility of a hierarchical design for a phylogenetic microarray in enabling the inclusion of previously uncharacterized oligonucleotide probes with phylogenetic predictive power. In interpreting data from a phylogenetic microarray hybridization, it is important to consider the potential biases inherent in a PCR and hybridization-based approach. To begin with, PCR of 16S rRNA genes extracted from a microbial assemblage will preferentially amplify the most abundant bacterial taxa. This biased representation within a heterogeneous rRNA gene amplicon will be reflected in the downstream analysis, whether that is rRNA gene clone library sequencing, denaturing gradient gel electrophoresis, or phylochip hybridization (Holben et al., 2004). The selection of primers with which to amplify the targeted bacterial taxa is also a significant variable. In this study, the combined use of Acidobacteria-specific primer 31F with universal Bacteria primer 1492R was appropriate given that the WMARS soil sample had been previously characterized. In future work using previously uncharacterized soils samples, it is advisable to use newly developed Acidobacteria-specific primer sets that will be inclusive of more Acidobacteria subdivisions (Barns et al., 2007). The potential for chimeric rRNA gene amplicons is also a concern, given that use of a Chimera Check program is not possible using phylochip hybridization data. As in this study, validation of hybridization results using quantitative PCR or other independent method is advisable to confirm and extend the hybridization observations. Lastly, using a massively parallel hybridization design requires use of thousands of oligonucleotide probes each with distinct hybridization kinetics. Given that it is not feasible to independently validate each probe using a positive control target rRNA gene, this study adopted an in silico analysis of probe predicted thermodynamic characteristics, the results of which did not support the rejection of probes based solely on in silico analyses. This latter observation is likely the beneficial result of including many probes targeting each phylogenetic clade, which reduces the respective contribution of probes that may have some degree of self-complementarity or inefficient hybridization kinetics under the selected hybridization conditions. The second hypothesis tested in this study was that a phylogenetic microarray could be used to identify changes in the relative abundance of bacterial taxa within a soil fraction. Since the relative
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abundance of Acidobacteria taxa may be altered in different soil microhabitats, it was of interest to compare the results of an Acidobacteria microarray hybridization from a whole soil sample versus the clay fraction from that same soil sample. The microarray hybridizations from both whole soil and a clay fraction of this soil revealed many similarities, including a higher non-specific hybridization and two distinct Acidobacteria lineages (i.e., 4.1.2.2.2.2.2.2.1 and 6.2.3.1.1.2.1) whose presence was strongly supported both by the magnitude of the mean hybridization signals relative to the mean value of all probes, and by the number of separate sub-lineages that were positive in either case. Also interesting was the apparent decrease in the hybridization signal for each of the phylogenetic groups in lineage 6.2.3.1.1.2.1 from the clay fraction hybridization relative to the whole soil results. It should be noted that the mean probe hybridization signals cannot be taken to be indicative of the quantitative abundance of any phylogenetic group present in an environmental sample, due to potential kinetic differences between individual probes; however, if a comparison is conducted of the same set of probes with different environmental samples, a shift in probe hybridization strength from one sample to another could indicate a real shift in bacterial relative abundance. These data therefore predict that, within the clay fraction of WMARS soil, a lower relative abundance of subdivision 6 bacterial taxa would be observed. To test the hypothesis that there is a difference in the relative abundance of Acidobacteria subdivisions within a clay fraction of soil versus bulk soil, quantitative PCR was employed to compare the levels of subdivision 4 and subdivision 6 rRNA gene templates within the whole WMARS soil and in the different soil fractions. In the bulk soil and in all soil fractions the quantitative PCR results indicated that subdivision 6 taxa were present at higher levels than subdivision 4 taxa. These results are supported by previous results from a 16S rRNA gene survey from this same soil sample, which recorded a higher relative abundance of subdivision 6 rRNA gene clones than subdivision 4 rRNA gene clones (Liles et al., 2003). Even though subdivision 6 taxa were present at high levels in every soil fraction, the soil clay fraction revealed a significant decrease in the quantitative PCR signal for subdivision 6 taxa, so that the ratio of subdivision 4 taxa to subdivision 6 taxa increased substantially in the clay fraction. Therefore, the predictive power of the phylogenetic microarray in revealing a previously unpredicted distribution of Acidobacteria taxa within a clay fraction was supported by the quantitative PCR analysis. Our collective understanding of soil Acidobacteria taxa will be enhanced both through recent culture-based and genomic studies (Ward et al., 2009), and through a better understanding of their distribution in soils and soil microhabitats. Severe reductions in the abundance and/or diversity of soil Acidobacteria have been observed in response to soil pollutants such as 2,4,6-trinitrotoluene (George et al., 2009), phenylurea herbicides (El Fantroussi et al., 1999), or hydrocarbons (2005). In contrast, soil Acidobacteria taxa have been observed to be abundant in PCB- and parathion-polluted soils (Nogales et al., 1999; Debarati et al., 2006), suggesting the capacity for some Acidobacteria taxa to degrade specific soil pollutants. Furthermore, it would be of interest to use a phylochip approach to investigate the prevalence of Acidobacteria and other bacterial taxa in the inner- and outer-microaggregate soil fractions (Kim et al., 2008), considering that Acidobacteria were found to have a reduced abundance within the inner-aggregate fraction in multiple soils (Mummey et al., 2006). Both human activities and natural ecosystem processes may have substantial effects on the abundance, diversity, and/or metabolic activities of soil Acidobacteria, and the availability of such a rapid means to detect diverse Acidobacteria (and other bacterial) taxa present in soil microbial assemblages can be a powerful tool to investigate changes in soil bacterial population structure. This study
demonstrates the feasibility of this phylogenetic microarray approach using positive control amplicons and a soil sample that had been previously studied. Future work will expand beyond the division Acidobacteria to encompass a broader phylogenetic diversity of soil bacteria, and may be applied to many different studies of soil microbial ecology. Acknowledgements We thank the other members of the Goodman and Liles laboratories for their support and critical analysis of this work, and Dr. Ena Urbach for useful scientific discussions. The gene expression center at the University of Wisconsin is thanked for all of their technical help in this work. This research was funded by grants from the David and Lucille Packard Foundation, the McKnight Foundation, NSF DEB-0213048, and Auburn University's Department of Biological Sciences, College of Sciences and Mathematics, and Vice President for Research. References Amann, R.I., Ludwig, W., Schleifer, K.H., 1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiological Reviews 59, 143e169. 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. Proceedings of the National Academy of Sciences of the United States of America 91, 1609e1613. Barns, S.M., Takala, S.L., Kuske, C.R., 1999. Wide distribution and diversity of members of the bacterial kingdom Acidobacterium in the environment. Applied and Environmental Microbiology 65, 1731e1737. Barns, S.M., Cain, E.C., Sommerville, L., Kuske, C.R., 2007. Acidobacteria phylum sequences in uranium-contaminated subsurface sediments greatly expand the known diversity within the phylum. Applied and Environmental Microbiology 73, 3113e3116. Billor, N., Hadi, A., Velleman, P., 2000. BACON: blocked adaptive computationallyefficient outlier nominators. Computational Statistics and Data Analysis 34, 279e298. Bintrim, S.B., Donohue, T.J., Handelsman, J., Roberts, G.P., Goodman, R.M., 1997. Molecular phylogeny of Archaea from soil. Proceedings of the National Academy of Sciences of the United States of America 94, 277e282. Brodie, E.L., DeSantis, T.Z., Joyner, D.C., Baek, S.M., Larsen, J.T., Andersen, G.L., Hazen, T.C., Richardson, P.M., Herman, D.J., Tokunaga, T.K., Wan, J.M., Firestone, M.K., 2006. Application of a high-density oligonucleotide microarray approach to study bacterial population dynamics during uranium reduction and reoxidation. Applied and Environmental Microbiology 72, 6288e6298. Burgmann, H., Pesaro, M., Widmer, F., Zeyer, J., 2001. A strategy for optimizing quality and quantity of DNA extracted from soil. Journal of Microbiology Methods 45, 7e20. Debarati, P., Pandey, G., Meier, C., van der Meer, J.R., Jain, R.K., 2006. Bacterial community structure of a pesticide-contaminated site and assessment of changes induced in community structure during bioremediation. FEMS Microbiology Ecology 57, 116e127. Dojka, M.A., Harris, J.K., Pace, N.R., 2000. Expanding the known diversity and environmental distribution of an uncultured phylogenetic division of bacteria. Applied and Environmental Microbiology 66, 1617e1621. El Fantroussi, S., Verschuere, L., Verstraete, W., Top, E.M., 1999. Effect of phenylurea herbicides on soil microbial communities estimated by analysis of 16S rRNA gene fingerprints and community-level physiological profiles. Applied and Environmental Microbiology 65, 982e988. Felsenstein, J., 1993. PHYLIP (Phylogeny Inference Package), Version 3.5c. Distributed by the Author. Department of Genetics, University of Washington, Seattle. George, I.F., Liles, M.R., Hartmann, M., Ludwig, W., Goodman, R.M., Agathos, S.N., 2009. Changes in soil Acidobacteria communities after 2,4,6-trinitrotoluene contamination. FEMS Microbiology Letters 296, 159e166. Head, I.M., Saunders, J.R., Pickup, R.W., 1998. Microbial evolution, diversity, and ecology: a decade of ribosomal RNA analysis of uncultivated microorganisms. Microbial Ecology 35, 1e21. Holben, W.E., Feris, K.P., Kettunen, A., Apajalahti, J.H.A., 2004. GC fractionation enhances microbial community diversity assessment and detection of minority populations of bacteria by denaturing gradient gel electrophoresis. Applied and Environmental Microbiology 70, 2263e2270. Hugenholtz, P., Goebel, B.M., Pace, N.R., 1998a. Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity. Journal of Bacteriology 180, 4765e4774. Hugenholtz, P., Pitulle, C., Hershberger, K.L., Pace, N.R., 1998b. Novel division level bacterial diversity in a Yellowstone hot spring. Journal of Bacteriology 180, 366e376.
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