Comparison of Different Denaturing Gradient Gel Electrophoresis Primer Sets for the Study of Marine Bacterioplankton Communities

An annual seasonal cycle of composition of a bacterioplanktoncommunity in an oligotrophic coastal system was studied by denaturinggradient gel electrophoresis (DGGE) using five different primersets. Analysis of DGGE fingerprints showed that primer set 357fGC-907rMgrouped samples according to seasons. Additionally, we usedthe set of 16S rRNA genes archived in the RDPII database tocheck the percentage of perfect matches of each primer for themost abundant bacterial groups inhabiting coastal plankton communities.Overall, primer set 357fGC-907rM was the most suitable for theroutine use of PCR-DGGE analyses in this environment.

INTRODUCTION

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INTRODUCTION

Denaturing gradient gel electrophoresis (DGGE) (11, 12) hasbecome one of the most frequently used methods for molecularfingerprinting. Although it is used widely in different areasof research (5, 7, 8, 10, 14), there seems to be little testingor justification for the correct choice of primers for eachtype of microbial community.

In the present work we have compared the PCR-DGGE profiles ofa bacterioplankton community during a seasonal cycle in BlanesBay Microbial Observatory by using five different primer sets.Seasonality of the bacterial assemblage in Blanes Bay has beenstudied previously by Schauer et al. (15) by use of DGGE witha single set of primers. That analysis showed that changes inthe dominant bacterial members over the sampling period occurredat a gradual pace, without abrupt changes in composition. AlternativePCR-dependent (clone libraries) and PCR-independent (catalyzedreporter deposition-fluorescent in situ hybridization) studiesof bacterioplankton seasonality have also been carried out inBlanes Bay (1), demonstrating that a detailed picture of seasonalitycan be obtained by combining several molecular approaches. Oceanicbiogeochemical conditions have been shown to correctly predictthe presence of annually recurring bacterial communities (6),thus suggesting that a link between these parameters exists.

A seasonal cycle provides a template for the temporal dynamicsof the community, since samples from the same season are expectedto have more similar fingerprints than samples that are distantin time. Thus, the most suitable set of primers will be theone that better reflects such seasonality. We optimized DGGEconditions for five primer sets commonly used in the literatureand evaluated the community compositions inferred from the DGGEprofiles. Additionally, we checked the number of perfect matchesof each of the primers to 16S rRNA genes in the RDPII database(release 9.39). The results provide information about the mostsuitable DGGE conditions and primer sets for amplification ofbacterioplankton communities.

Sampling.

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Sampling.

Surface seawater samples (first 20 to 50 cm) were collectedmonthly during one seasonal cycle (from February 2003 to March2004) from the Blanes Bay Microbial Observatory on the Catalancoast (northwest Mediterranean), 70 km north of Barcelona, Spain.Samples were taken 1 km offshore (41°40'N, 2°48'E),and seawater was kept in 25-liter polycarbonate carboys forless than 2 h until processing.

To collect bacterioplankton biomass, 5 liters of seawater wasfiltered by use of a peristaltic pump through a 3-µm-pore-sizepolycarbonate filter and a 0.2-µm Sterivex filter (Durapore;Millipore) in succession. The Sterivex unit was filled with1.8 ml of lysis buffer and stored at –80°C.

PCR-DGGE fingerprinting.

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PCR-DGGE fingerprinting.

Nucleic acid extraction was performed as described by Massanaet al. (9). Five different sets of primers were tested in orderto obtain fragments of the 16S rRNA gene suitable for DGGE analysis.Their sequences, as well as the annealing temperatures and DGGEconditions used in this study, are detailed in Table 1 (forPCR details, see the supplemental material).

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TABLE 1. Primers and PCR and DGGE conditions used in this study

DGGE analysis was run with a DCode (Bio-Rad) and a CBS systemas previously described by Muyzer et al. (12). Nine hundrednanograms of PCR product was loaded for each sample.

Perpendicular DGGE analyses were carried out in order to selectappropriate electrophoresis conditions for each set of primers.One thousand five hundred nanograms of PCR product from differentcultures corresponding to Roseobacter sp. strain MED193, Polaribactersp. strain MED152, and Dokdonia sp. strain MED134 obtained fromthe culture collection of the Blanes Bay Microbial Observatorywas applied across the entire width of the gel and electrophoresedat 150 V for about 3 to 5 h. At a denaturant concentration rangeof 45 to 65% (63fGC-518r), 50 to 65% (357fGC-907rM), 45 to 65%(357fGC-518r), 45 to 60% (968fGC-1401r), or 50 to 60% (1055f-1392rGC),the three PCR products displayed reduced mobility. We thus confirmedthat a gradient of 40 to 80% was adequate for all primers.

Using this 40 to 80% gradient, we ran five different DGGE gelsincluding the 14 coastal samples from Blanes Bay obtained duringa 1-year cycle (Fig. 1).

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FIG. 1. DGGE fingerprints with different primer sets of bacterial assemblages obtained from Blanes Bay during a seasonal cycle (lane 1, February 2003; lane 2, March 2003; lane 3, April 2003; lane 4, May 2003; lane 5, June 2003; lane 6, July 2003; lane 7, August 2003; lane 8, September 2003; lane 9, October 2003; lane 10, November 2003; lane 11, December 2003; lane 12, January 2004; lane 13, February 2004; lane 14, March 2004).

Quantitative analyses.

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Quantitative analyses.

Digitized DGGE images were analyzed with Quantity One software(Bio-Rad) (for details, see the supplemental material). Bandsoccupying the same position in the different lanes of the gelswere identified. Primer set 357fGC-907rM retrieved a largernumber of bands both globally (462 bands) and in each sample(average, 33.0 bands).

A matrix was constructed for all lanes, taking into accountthe presence or absence of the individual bands and the relativecontribution of each band (by percentage) to the total intensityof the lane. This matrix was used to calculate a distance matrixusing normalized Euclidean distances (root mean square differences)with the software STATISTICA. Finally, a dendrogram comparingsamples for each set of primers was obtained by use of the unweighted-pairgroup method using average linkages and STATISTICA (Fig. 2).Primer set 357fGC-907rM clustered the samples according to differentseasons. In contrast, other primers did not show a completelycoherent seasonal clustering.

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FIG. 2. Euclidean-distance dendrograms generated from the DGGE profiles of the 14 samples analyzed for each primer set, determined by the unweighted-pair group method using average linkages. The scale bar is linkage distance and applies to all dendrograms. To clarify interpretation, the season of each sample is indicated (W, winter; Sp, spring; Su, summer; A, autumn). Dates during which samples were obtained are indicated by month and year.

Statistical analyses.

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Statistical analyses.

We used ancillary physical and ecological data from the BlanesBay Observatory to run one-way analyses of variance (ANOVA)according to the classification based on DGGE clusters. Fordetails of statistical analyses, see the supplemental material.

As can be seen in Table 2, most variables, particularly thephytoplankton-related variables, nutrients, and other physicalvariables, showed that the seasonal classification carried outby DGGE with primer set 357fGC-907rM explained the variabilityin the data. Significant exceptions were NH₄ and bacterial abundance,two variables that seemed to be particularly well buffered inthis and in most other systems (L. Alonso-Sáez, E. Vázquez-Domínguez,J. Pinhassi, C. Cardelús, M. M. Sala, I. Lekunberri,M. Vila-Costa, F. Unrein, R. Massana, R. Simó, and J.M. Gasol, submitted for publication). A post hoc Tukey-Kramertest classified summer as the most distinct season of the year(Table 2), in the same way as the DGGE dendrogram based on primerset 357fGC-907rM separated summer from the other seasons (Fig.2).

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TABLE 2. Results of ANOVA done with the Blanes Bay Microbial Observatory physical and microbiological data^a

We also tested whether the clustering generated by the otherprimer sets explained the seasonality of the variables. In mostcases (70%), the F-value of ANOVA (used as an indicator of theability of the clustering to organize the ecological data) washigher with primer set 357fGC-907rM than with any of the othersignificant ordinations with the other primers. In 20% of thecases, primer set 63fGC-518r organized better than the othersets of primers, and in 10% of the cases the best was primerset 357fGC-518r. Therefore, the first set (357fGC-907rM) orderedthe environmental variables in an ecologically coherent fashion.

Probe match with RDPII database.

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Probe match with RDPII...

Additionally, the specificities of the primers selected forour study were analyzed against the RDPII database (release9.39, 2006 [http://rdp.cme.msu.edu/]), which contained 90,211bacterial sequences with almost the complete 16S rRNA gene sequence( $≥$ 1,200 bp). The numbers of sequences in the complete databasewith no mismatches varied widely. Three primers matched morethan 75% of the sequences from the whole database (357f, 518r,and 907rM).

The percentages of sequences for the most representative groupsof bacteria common in coastal bacterioplankton communities (Alphaproteobacteria[mostly from the SAR11 and Roseobacter clades], Gammaproteobacteria,Bacteroidetes [Cytophaga-Flavobacterium-Bacteroides, or CFB],and cyanobacteria) targeted by each primer were also different(Fig. 3). Some primers (518r, 357f, 907rM, and 1392r) matchedthese abundant groups of bacteria in Blanes Bay, while otherprimers (63f, 968f, 1055f, and 1401r) missed some of these groups,such as members of the Roseobacter (968f and 1401r), SAR11 (968f),and CFB (63f, 968f, 1401r, and 1055f) groups. However, it hasto be taken into account that primers 63f and 1392r target theinitial and final regions of the rrs gene, respectively, andthat, although we have considered for the analysis of probematching only sequences of more than 1,200 bp, the matchingpercentages for these primers are likely underestimates.

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FIG. 3. Histograms of matching percentages for the most abundant phylogenetic groups in coastal bacterioplankton obtained from data in the RDPII database, release 9.39 (sequences with $≥$ 1,200 bases, 0 mismatches), for each primer used in this study ( ${alpha}$ , Alphaproteobacteria; ${gamma}$ , Gammaproteobacteria). Percentages have been calculated on the basis of 8,651 sequences of Alphaproteobacteria, 45 sequences of Roseobacter sp., 54 sequences of the SAR11 cluster, 13,993 sequences of Gammaproteobacteria, 33,203 sequences of proteobacteria, 12,021 sequences of CFB, and 1,812 sequences of cyanobacteria.

We also included primer 907r used by Schauer et al. (15) inthe comparison. This primer differs by only 1 base from primer907rM and does not perfectly match either the SAR11 clusteror some Gammaproteobacteria.

Although primer pair 357fGC-518r should be suitable to describemarine bacterioplankton, given that it matches with almost allsequences in the RDPII database, the quantitative analysis ofthe DGGE fingerprint showed that this set was not the best combinationfor Blanes Bay, since it did not reflect the seasonality ofbacterial assemblage composition and the short length of theamplicon obtained limits the phylogenetic information containedin the sequenced bands.

Primer set 357fGC-907rM matched most of the groups present inBlanes Bay and seemed a priori a good choice for describingthe bacterial community. Analysis of the DGGE fingerprints showedthat primer set 357fGC-907rM was also the combination that betterreflected the seasonal changes in the plankton of Blanes Bay.These primers generated clusters that were consistent with seasons(Fig. 2) and that corresponded to changes in the physicochemicaland phytoplankton variables (Table 2).

In conclusion, the primer set 357fGC-907rM, which amplifiesthe V3 to V5 region of rrs genes, is recommended for the routineuse of PCR-DGGE analyses of bacterioplankton samples at leastfrom coastal Mediterranean waters but probably also from othercoastal and open sea environments since the bacterial compositionsin all of these systems are rather similar (1, 15). However,determination of the most suitable set of primers should becarried out for every habitat. Once appropriate fingerprintshave been obtained, the most interesting samples can be selectedwith confidence for in depth analysis by other techniques, suchas fluorescent in situ hybridization or clone libraries.

ACKNOWLEDGMENTS

This work was supported by the Spanish projects MicroDiff (REN2001-2110/MAR),ESTRAMAR (CTM2004-12631/MAR), GENMµMAR (CTM2004-02586/MAR),TRAGUA (CSD2006-00044), TEC2006-13109-C03-02/MIC, MODIVUS (CTM2005-04975/MAR),and NoEs MARBEF and Marine Genomics Europe.

We thank E. L. Sà for her help in the lab, V. Balaguéand I. Forn for their help with field sampling and lab support,K. Jürgens for his useful comments, L. Alonso-Sáezfor access to her unpublished data, and all coworkers at Blanesfor sharing the data.

FOOTNOTES

* Corresponding author. Mailing address: Departament de Genètica i Microbiologia, Facultat de Ciències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. Phone: 34-935813011. Fax: 34-935812387. E-mail: olga.sanchez{at}uab.es

Published ahead of print on 27 July 2007.

Supplemental material for this article may be found at http://aem.asm.org/.

REFERENCES

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