INTRODUCTION

Top Abstract Introduction Materials and Methods Results Discussion References

rRNA intergenic spacer analysis (RISA) is a method of microbial community analysis which provides estimates of microbial diversity and community composition without the bias imposed by culture-based approaches or the labor involved with small-subunit rRNA gene clone library construction. RISA was used originally to contrast diversity in soils (6) and more recently to examine microbial diversity in the rhizosphere and marine environments (1, 22). The method involves PCR amplification from total bacterial community DNA of the intergenic region between the small (16S) and large (23S) subunit rRNA genes in the rRNA operon, with oligonucleotide primers targeted to conserved regions in the 16S and 23S genes. The 16S-23S intergenic region, which may encode tRNAs depending on the bacterial species, displays significant heterogeneity in both length and nucleotide sequence. Both types of variation have been extensively used to distinguish bacterial strains and closely related species (4, 11, 14, 20, 23). In RISA, the length heterogeneity of the intergenic spacer is exploited. The PCR product (a mixture of fragments contributed by community members) is electrophoresed in a polyacrylamide gel, and the DNA is visualized by silver staining. The result is a complex banding pattern that provides a community-specific profile, with each DNA band corresponding to at least one organism in the original assemblage.

Although RISA provided relatively rapid estimates of microbial community composition, in practice polyacrylamide gel electrophoresis tended to be time-consuming and cumbersome. In addition, because silver staining is a relatively insensitive method of DNA detection, large amounts of PCR product were necessary for analysis and resolution tended to be low. The limitations of the existing methodology led us to develop an improved version of RISA, which we refer to as automated RISA (ARISA). This method is similar to the recently published terminal restriction fragment length polymorphism and length heterogeneity analysis by PCR community analysis techniques (13, 24). In the automated approach, the initial steps of DNA extraction and PCR amplification are the same as in RISA, except that PCR is conducted with a fluorescence-tagged oligonucleotide primer. The electrophoretic step is subsequently performed with an automated system, which provides laser detection of fluorescent DNA fragments. ARISA-PCR may generate DNA fragments up to 1,400 bp in length (6). Discrimination of these larger size fragments represented a new application for the capillary electrophoresis system employed.

In this work, the sensitivity and reproducibility of ARISA were demonstrated through application of the technique to natural freshwater bacterial communities from three sites in northern Wisconsin. In addition, information on the length heterogeneity of the 16S-23S intergenic spacer among cultured organisms available through the GenBank database was evaluated, so as to gain a better understanding of the potential biases inherent in the estimates of bacterial diversity that ARISA provides. Overall, the results suggest that ARISA is a rapid and effective method for assessing microbial community diversity and composition that can be especially useful at the fine spatial and temporal scales necessary in ecological studies.

	MATERIALS AND METHODS

Top Abstract Introduction Materials and Methods Results Discussion References

Study sites and sample collection. Samples were collected in June, August, and September 1998 from Crystal Bog Lake and Sparkling Lake in the Northern Highland Lake District in northern Wisconsin (Oneida County) and Lake Mendota, adjacent to the University of WisconsinMadison campus in southern Wisconsin (Dane County). All three lakes are part of the North Temperate Lakes Long-Term Ecological Research Site. Crystal Bog Lake is a shallow (maximum depth, 2.5 m), dystrophic lake; Sparkling Lake is an oligotrophic lake; and Lake Mendota is a deep, eutrophic lake. Sparkling Lake and Lake Mendota were stratified at the time of sampling. In Crystal Bog and Sparkling Lakes, whole water samples were collected above the point of maximum depth by using either a Van Dorn or integrated water column sampler. In Lake Mendota, surface grab samples were taken at the end of a pier in front of the Center for Limnology on the University of Wisconsin campus. Whole water samples were screened through a 10-µm-pore-size nylon mesh (Spectrum) in the field, transported to the laboratory on ice, and concentrated in aliquots of 400 ml to 1 liter onto 0.2-µm-pore-size filters (Supor-200; Gelman). The filtration apparatus was rinsed with filter-sterilized, deionized water and sample water in between samples. Filters were placed in cryovials, frozen immediately in liquid nitrogen, and stored at 80°C until further processing.

DNA extraction and purification. DNA was extracted and purified by using a modification of the FastPrep bead beater method (Bio 101), following Borneman et al. (5). In the homogenization step, one half of each filter was placed into separate extraction tubes after first cutting the filter half into several pieces. The mixture was shaken with a single large bead on the FastPrep instrument (Bio 101) for 30 s at speed 5. By comparing the extracted DNA to known quantities of standard DNA in an agarose gel, this homogenization procedure was found to result in the highest yields of DNA without excessive shearing. In the final steps of purification, DNA extracted from the two filter halves for each sample was pooled.

ARISA. ARISA-PCR was performed following the method of Borneman and Triplett (6) with modifications. Reaction mixtures contained 1× PCR buffer (Promega), 2.5 mM MgCl₂, 500 µg of bovine serum albumin per ml, a 200 µM concentration of each dNTP, a 400 µM concentration of each primer, 2.5 U of Taq polymerase, and approximately 350 ng of template DNA in a final volume of 50 µl. The primers were 1406f, 5' TGYACACACCGCCCGT 3' (universal, 16S rRNA gene), and 23Sr, 5' GGGTTBCCCCATTCRG 3' (bacterial-specific, 23S rRNA gene), and primer 1406f was 5' end labeled with the phosphoramidite dye 5-FAM. Reaction mixtures were held at 94°C for 2 min, followed by 30 cycles of amplification at 94°C for 15 s, 55°C for 15 s, and 72°C for 45 s and a final extension of 72°C for 2 min. To investigate the effect of the PCR cycle number on ARISA profiles, PCR was also performed with 15, 20, and 25 rounds of amplification by using samples from Crystal Bog and Sparkling Lakes. Reaction volumes were 10, 20, and 50 µl, with reagent concentrations as described above, except for the template DNA, which was increased by 1.5 to 3 times the usual amount.

Cloning and sequencing of ARISA-PCR products. An unlabeled ARISA-PCR product from an integrated water column sample collected in the Sparkling Lake hypolimnion (SH) was chosen for cloning and sequencing because of the complexity of its profile. Products from two replicate PCRs were pooled and purified by using the Wizard DNA purification kit (Promega). Purified DNA was then cloned into vector pSTBlue-1 by using the Novagen Perfectly Blunt cloning kit and the clones screened for -complementation with X-Gal (5-bromo-4-chloro-3-indolyl--D-galactopyranoside) and IPTG (isopropyl--D-thiogalactopyranoside). Plasmid DNA was isolated from positive colonies by using the Qiaprep Spin Miniprep kit (Qiagen) and was digested with EcoRI to verify the presence of inserts.

Database examination. The GenBank database (National Center for Biotechnology Information) was searched for information on the length heterogeneity of the 16S-23S rRNA intergenic region among described microbial taxa. The lengths of 307 16S-23S intergenic spacer sequences, which were accessed by searching with the Entrez browser, were determined. Included in the analysis were data for species of both Bacteria and Archaea, for multiple strains within single species, and for multiple rRNA operons within the genomes of the same organisms. When multiple sequences were recorded for the same species or strain, if the sequences were of different length or were clearly labeled as different operon versions (e.g., rrnA and rrnB) but were of the same length, they were included in the analysis. However, multiple sequences reported for the same strain were excluded if they did not differ in length and were labeled only as different clones.

Nucleotide sequence accession numbers. The nucleotide sequences of the cloned ARISA-PCR fragments that were sequenced in both directions and whose lengths are reported in Table 1 were deposited in the GenBank database and given accession no. AF164144 to AF164150.

	RESULTS

Top Abstract Introduction Materials and Methods Results Discussion References

Database examination. We examined 307 16S-23S intergenic spacer sequences from the GenBank database contributed by approximately 60 genera and 165 species of prokaryotes belonging to the Archaea domain and 8 major lineages of the Bacteria domain. However, the majority of currently available sequences are from taxa belonging to either the gram-positive phyla or the -proteobacteria. Sequences from these two groups comprised roughly 60 and 20%, respectively, of the total number examined. The reason for the dominance of these groups in the current database is likely due to the large number of medically important organisms within these lineages. Because of the utility of intergenic spacer heterogeneity for distinguishing closely related strains and species, analysis of this region is increasingly used to identify clinical isolates which are often difficult to distinguish phenotypically (9). Thus, because we wanted to include as many sequences as possible in our examination of the database, overrepresentation of these taxa in our analyses was unavoidable.

View larger version (32K): [in this window] [in a new window]   FIG. 1.   Frequency histogram of 307 16S-23S intergenic spacer lengths (in base pairs) contributed by approximately 60 genera and 165 species of prokaryotes. The data were compiled from the GenBank database.

Cloning and sequencing of the ARISA-PCR product. Eighteen clones of an unlabeled ARISA-PCR product from a sample collected in the SH were sequenced, to confirm that the 16S-23S intergenic spacer was being amplified in the freshwater environmental samples. The SH sample was chosen because of its relatively high complexity; there were approximately 50 fragments in the ARISA profile from this site. All of the clones were sequenced from the 16S end of the PCR product, and 10 of the 18 were also sequenced from the 23S end. The partial 16S and/or 23S rRNA gene sequences (~100 to 140 bp of each) were then submitted separately to BLAST. The names and accession numbers of cultured organisms that most closely matched each of the clones in 16S rRNA gene sequence (calculated by BLAST as percent similarity), as well as their tentative phylogenetic placements, are given in Table 1.

Applications of ARISA to natural freshwater bacterial communities. ARISA was used to assess the composition and diversity of three freshwater bacterial communities in eplimnetic samples from Crystal Bog Lake (CB) and Lake Mendota (LM) and in a metalimnetic sample from Sparkling Lake (SM), which provided a range of community complexity. For a single sample from each site, three to four PCR replicates were performed. The ARISA profiles (electropherograms) for two representative PCRs from each lake are shown in Fig. 2. In general, PCR reproducibility was very high, as evidenced by the fact that the electropherograms representing the duplicate PCRs for each site are almost entirely superimposed. The only major variation observed was for the CB site, in which two strong products were generated in one of the PCR replicates that were absent or nearly so from the other two (data not shown). Subsequently, a fourth replicate was performed that gave the profile in Fig. 2A.

View larger version (34K): [in this window] [in a new window]   FIG. 2.   Partial ARISA profiles of the bacterial communities in Crystal Bog Lake (A), Lake Mendota (B), and Sparkling Lake (C) during the summer of 1998. In each panel, the red and black electropherograms represent duplicate PCRs that were performed on a single sample from each site.

View larger version (30K): [in this window] [in a new window]   FIG. 3.   Partial ARISA profiles of the bacterial communities in Crystal Bog Lake. (A) Red and black electropherograms representing samples collected from depths of 1 and 2 m, respectively, in June 1998. (B) Red and black profiles representing samples collected from a depth of 1 m in September and June 1998, respectively.

Influence of PCR cycle number on ARISA profiles. One of the greatest improvements of ARISA over the previous method is the increased sensitivity of DNA detection so that considerably less PCR product is needed for analysis. We took advantage of this feature to investigate how ARISA profiles might change as the PCR cycle number was reduced from 30, the number of amplification cycles normally used, to 15. In doing so, we were most interested in determining whether the pattern and number of detectable ARISA-PCR fragments would change with cycle number, thus influencing estimates of community composition and diversity. In this experiment, template DNA from CB, representing a relatively low complexity sample, and SH, which provided a more complex banding pattern, were used. In general, overall ARISA patterns were similar between PCRs performed with varying numbers of amplification cycles, especially for products that were present in higher concentrations. For example, the distribution of major peaks in the SH sample with 15, 20, and 25 rounds of amplification was highly reproducible (Fig. 4). As might be expected, however, the total number of fragments tended to be more sensitive to changes in cycle number because of the large number of fragments near the limit of detection (i.e., between 50 and 100 fluorescence units). For example, a series of eight and nine fragments ranging in size from ~460 to 540 bp in the CB profile were consistently observed with 30 rounds of amplification but were undetectable at 25 (Fig. 5).

View larger version (29K): [in this window] [in a new window]   FIG. 4.   Results from an experiment in which the effect of the PCR cycle number on ARISA profiles was investigated by using a sample collected from the SH in September 1998. PCR was performed with 15, 20, and 25 rounds of amplification, giving the results displayed in panels A, B, and C, respectively.

View larger version (22K): [in this window] [in a new window]   FIG. 5.   Results from an experiment in which the effect of the PCR cycle number on ARISA profiles was investigated by using a sample collected from Crystal Bog Lake in September 1998. The red and black electropherograms represent PCRs that were performed with 25 and 30 cycles of amplification, respectively.

	DISCUSSION

Top Abstract Introduction Materials and Methods Results Discussion References

The need for a more rapid and reproducible method of microbial community analysis led us to develop an automated version of RISA, which previously relied on manual polyacrylamide gel electrophoresis and DNA detection by silver staining. ARISA has several advantages over the former method, thus increasing its utility as a technique for assessing the composition and diversity of naturally occurring bacterial communities. The new method was found to be very sensitive, thus requiring much less PCR product for analysis, and highly reproducible at all levels of replication tested. In addition, because of the technique's ease, the effect of altered PCR conditions (e.g., amplification cycle number) on ARISA profiles could be rapidly assessed. In this study, a capillary electrophoresis system was employed to resolve large (up to 1,200 bp) ARISA-PCR size fragments from freshwater environmental samples, representing a new application for this automated system. Gel-based, automated DNA sequencing technology can also be used to discriminate large size fragments (16) and thus can also be applied to ARISA.

The results obtained with ARISA should be cautiously interpreted. As a molecular technique that relies upon total community DNA extraction and PCR amplification, ARISA is subject to the usual systematic biases introduced by these procedures (21, 25). In addition, specific biases associated with amplification of the 16S-23S intergenic region, such as possible preferential amplification of shorter templates and biases imposed by secondary structure or DNA flanking the template region (10), have not been well investigated. For these reasons, any conclusions regarding the relative abundance of bacterial populations represented in the ARISA profiles should be carefully made.

In addition to the above concerns, the relationship between the number of fragments in an ARISA profile and the absolute diversity of the community it represents requires further investigation. On the one hand, overlapping intergenic spacer size classes among unrelated organisms contributing to the profile may lead to underestimates of diversity. At the same time, interoperonic differences in spacer length frequently occur within the genomes of cultured organisms (9, 19), and this is probably true for uncultivated microbes in environmental samples as well. Thus, single organisms are likely to contribute more than one peak to an ARISA profile. The degree to which this occurs is currently unknown but perhaps may be inferred from data on spacer size variability among cultured isolates. For example, Jensen et al. (11) examined spacer length heterogeneity among 300 strains of bacteria belonging to eight genera and 28 species by using PCR amplification of the region. In addition to finding that most strains within species displayed the same pattern of amplification products, they observed that the majority of species (85%) exhibited between only one and three PCR products (i.e., one to three spacer lengths). In addition, in Escherichia coli K-12 there are only two types of intergenic spacer regions among the seven operons, and several clones of one of these regions obtained from K-12 and six other E. coli strains were recently shown to be identical in length (7). As more information of this type becomes available, we should gain a much clearer understanding of the biases associated with diversity estimates by ARISA. For now, assuming biases remain fairly constant between samples, we suggest that by counting the total number of reproducible fragments in a profile, ARISA can be used to estimate the relative diversity among sites, as we did for the three lakes in this study.

Despite the drawbacks outlined above, our results indicate that ARISA can be effectively used to estimate community composition in natural samples, especially for fine-scale comparative purposes, and to detect community shifts with experimental manipulation. Due to the high reproducibility and sensitivity of the method, we found that electropherograms could be readily compared among sites, and their similarity levels could be easily and precisely quantified by using the GeneScan analysis software. Indices such as Sorenson's and Jaccard's are increasingly used to quantitatively assess the similarity among communities (12, 13, 17, 18). These indices will obviously be influenced by the number of DNA fragments present in a community fingerprint, which in turn is dependent upon factors such as the detection level of the technique used, and characteristics of the particular gene fragment amplified. It would therefore be interesting to compare results from ARISA and other commonly used methods of community analysis, such as denaturing gradient gel electrophoresis and terminal restriction fragment length polymorphism analyses.

ARISA can be further developed by employing phylum-level (or below) oligonucleotide primers in order to investigate the dynamics of specific phylogenetic groups, as was done by Robleto et al. (22) for a specific group within the -proteobacteria. Analysis of particular taxonomic groups rather than entire communities should result in much less complex fragment patterns, so that the biases discussed above could perhaps be more effectively investigated and reduced. In addition, several primers targeting different taxa could eventually be used on the same sample and the separate dynamics of each group could be evaluated simultaneously. Finally, although ARISA does not provide direct phylogenetic information on particular fragments in the profile, the precise sizing information afforded by the method could be used to identify the major bands of interest in a separate manual polyacrylamide gel. These fragments could then be further characterized through band excision and sequence analysis, as was performed by Robleto et al. (22). However, perhaps the most powerful and appropriate use of ARISA is as a rapid survey technique prior to, or in conjunction with, the application of more accurate but time-intensive methods for estimating community composition. Insight into ecological patterns gained from ARISA surveys at different spatial and temporal scales could allow for a more deliberate and effective application of techniques such as 16S rRNA gene cloning and sequencing and fluorescence in situ hybridization.