Bacterioplankton Community Composition in 67 Finnish Lakes Differs According to Trophic Status
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Vol. 62: 241–250, 2011 AQUATIC MICROBIAL ECOLOGY Published online February 8 doi: 10.3354/ame01461 Aquat Microb Ecol Bacterioplankton community composition in 67 Finnish lakes differs according to trophic status Eija Kolmonen1, Kaisa Haukka1, 3, Anne Rantala-Ylinen1, Pirjo Rajaniemi-Wacklin1, 4, Liisa Lepistö2, Kaarina Sivonen1,* 1University of Helsinki, Department of Food and Environmental Sciences, Division of Microbiology, Viikki Biocenter, PO Box 56, 00014 Helsinki University, Finland 2Finnish Environment Institute, PO Box 140, 00251 Helsinki, Finland 3Present address: National Institute for Health and Welfare, PO Box 30, 00271 Helsinki, Finland 4Present address: Finnish Red Cross Blood Service, Research and Development, Kivihaantie 7, 00310 Helsinki, Finland ABSTRACT: The bacterioplankton composition of 67 Finnish lakes was characterised by denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA gene fragments, and subsequent sequence analyses of major fragments. The DGGE patterns grouped as a function of environmental characteristics describing the trophic status of the lakes, such as total phosphorus (TP), total nitrogen (TN), TN:TP, chlorophyll a, Secchi depth and water colour. Most sequences retrieved represented Verrucomicrobia, Actinobacteria and Cyanobacteria, while those representing Alpha- and Gamma- proteobacteria, Chloroflexi and Acidobacteria were less frequent. The presence of several sequences could be linked to the trophic status of the lakes, while that of others was more common and thus unrelated to the trophic status. These results suggest that individual responses towards environmen- tal factors may occur among the bacterioplankton at the level of phyla as well as phylotypes. KEY WORDS: Bacterioplankton community composition · Lake trophic status · DGGE · Canonical correspondence analysis · CCA Resale or republication not permitted without written consent of the publisher INTRODUCTION The roles of the various factors governing bacterio- plankton distribution are under debate. The influence Bacterioplankton in freshwater ecosystems has a key of seasonality, such as water column mixing and ther- role in nutrient cycling. Thus, detailed understanding mal stratification, on bacterial community composi- of their distribution and the factors affecting bacterio- tion (BCC) is well established (Yannarell et al. 2003, plankton community composition are important. It has Shade et al. 2007, 2008, Nelson 2009). Several studies been suggested that relatively few groups of bacteria have revealed a statistical relationship between BCC dominate freshwater systems and that these groups and temperature (Van der Gucht et al. 2001, Muy- are widespread around the world (Zwart et al. 2002, laert et al. 2002, Yannarell et al. 2003, Jardillier et al. Stepanauskas et al. 2003). According to previous stud- 2004, Yannarell & Triplett, 2004, 2005, Lindström et ies, the most common freshwater bacteria belong to al. 2005, Kritzberg et al. 2006, Shade et al. 2007, the divisions Proteobacteria, Actinobacteria, Cyano- 2008). In addition, BCC has been shown to vary along bacteria and Verrucomicrobia and to the Cytophaga- gradients of pH (Lindström 2000, Lindström & Leski- Flavobacterium-Bacteroides group (Zwart et al. 2002). nen 2002, Stepanauskas et al. 2003, Lindström et al. In addition, it has been shown that lakes contain many 2005, Schauer et al. 2005, Yannarell & Triplett 2005, bacterial groups that are distinct from those found in Haukka et al. 2006), humic acid content (Haukka et the oceans (Zwart et al. 2002). al. 2005) and lake water retention time (Lindström et *Corresponding author. Email: [email protected] © Inter-Research 2011 · www.int-res.com 242 Aquat Microb Ecol 62: 241–250, 2011 al. 2005). Muylaert et al. (2002) suggested that BCC Nucleic acid extraction and PCR amplification. was dependent on the dominant substrate source DNA was extracted from the cells on 10, 5, 1 and together with food web structure in 4 shallow 0.2 µm filters using a modified hot phenol method and eutrophic lakes. Grazing, biomass of microzooplank- purified by Nucleotrap PCR purification (BD Bio- ton and viral infections play an important role in con- sciences) and QuickStep PCR Purification (Edge trolling BCC (Lindström 2000, 2001, Hahn & Höfle BIOSystems) kits as detailed previously by Rantala et 2001, Jürgens & Matz 2002, Kent et al. 2004, Van der al. (2006). Primers F-968-GC and R-1401 targeting Gucht et al. 2005). Jardillier et al. (2004) demon- eubacterial 16S rRNA genes (Nübel et al. 1996) were strated that BCC depends on the combined effects of used to amplify PCR products for DGGE. PCR reac- dominant substrate sources and selective predation tions were performed in a volume of 16 µl containing by bacterial consumers. Nutrient availability, biomass 1 µl of template DNA, 200 nM of primers F-968-GC of certain phytoplankton, lake productivity (concen- and R-1401, 200 µM dNTP solution, 1× PCR buffer, 1 M tration of chlorophyll a), water colour and trans- betaine (Sigma), 0.3 U DyNAzymeTM II DNA poly- parency (Secchi depth) and dissolved organic carbon merase (Finnzymes) and sterile water up to 16 µl. The all appear to influence the BCC (Methé & Zehr 1999, PCR programme consisted of a denaturing step at 94°C Lindström 2000, Yannarell & Triplett 2004, 2005, Van for 3 min, 35 cycles at 94°C for 1 min, 51°C for 1 min der Gucht et al. 2005). and 72°C for 2 min, and a final extension step at 72°C In the present study, we aimed to identify the fea- for 10 min. The size and purity of the amplification tures that best explained variation in BCC among 67 products were checked on a 1.5% agarose gel. Two boreal lakes in Finland. Denaturing gradient gel parallel PCR reactions from each sample were pre- electrophoresis (DGGE) of PCR-amplified 16S rRNA pared and combined after amplification. The com- gene fragments and subsequent sequence analyses of bined PCR products (approx. 80% of the reaction) specific fragments were used to characterise the bacte- were loaded into the DGGE gel. rial community structure in water samples of these DGGE. DGGE was performed on a DcodeTM Univer- lakes. These data were then analysed by multivariate sal Mutation Detection System (Bio-Rad) using 6% analysis in relation to physicochemical characteristics. polyacrylamide and 35 to 55% formamide-urea gradients as previously described by Kolmonen et al. (2004). The samples were run in 4 gels and each MATERIALS AND METHODS contained 3 standard lines (see Fig. S1 in the supple- ment at www.int-res.com/articles/suppl/a062p241_ Samples. Water samples included in this study origi- supp.pdf). Standards contained reamplified 16S rRNA nated from 67 lakes in southern and central Finland gene fragments from a lake sample selected to cover (see Table S1 in the supplement at www.int-res.com/ the migration range of the bands. The standards with 6 articles/suppl/a062p241_supp.pdf). From each lake, a band positions were applied for normalisation using composite sample from a depth of 0 to 2 m was col- Bionumerics software (Applied Maths BVBA, v 4.0). lected from an open lake area. Samples were collected DNA bands were excised from the DGGE gels with at the beginning of July or the middle of August with 1 a sterile scalpel and eluted from the gel slices using exception, which was collected on 16 September 2002 sterile water at 4°C overnight. These eluted products (Lake Vitträsk, no. 71). For DNA extraction, an aliquot were reamplified and the PCR products were sepa- of 75 to 1000 ml was filtered through a series of filters rated again in DGGE. In some cases, the band separa- with pore sizes 10, 5, 1 and 0.2 µm as detailed by tion step was performed twice to ensure proper band Rantala et al. (2006). separation. Several bands from different gels but the The physicochemical characteristics and cyano- same position were sequenced as well as run again to bacterial biomass of 55 and 50 lakes, respectively, have ensure that each band position represented only 1 been analysed previously (Rantala et al. 2006), and phylotype. From 46 band positions with sequenced these data were used in the statistical analyses. Cyano- bands, only 11 contained sequences from different bacterial composition of the lakes was analysed by phyla. Separated products from DGGE gels were microscopy, and cell counts were converted to reamplified with primers F-968 (without the GC- biovolumes using the cell volumes of the phytoplank- clamp) and R-1401. PCR products were purified using ton database of the Finnish Environment Institute Microcon PCR purification tubes (Millipore) and (www.environment.fi) as described by Rantala et al. sequenced using the Big Dye cycle sequencing ready (2006). Nineteen of the lakes were oligotrophic (total reaction kit (Applied Biosystems) according to the phosphorus content, TP < 10 µg l–1), 29 mesotrophic manufacturer’s instructions. Sequencing reactions (TP: 10 to 34 µg l–1), 15 eutrophic (TP: 35 to 100 µg l–1) were analysed with the Applied Biosystems 310 and 4 hypertrophic (TP > 100 µg l–1). genetic analyser. Kolmonen et al.: Bacterioplankton community composition in 67 Finnish lakes 243 Sequence analysis. The partial 16S rRNA gene RESULTS sequences derived from DGGE gels were analysed using NCBI-BLAST (Altschul et al. 1997) in order to Lake characteristics identify the most similar sequences in the GenBank database (April 2009). Taxonomical classification of The lakes sampled are situated in southern and central the bacterial sequences was confirmed with the Classi- Finland between northern latitudes 60° 10’ and 62° 50’ fier analysis tool (Ribosomal Database Project II, Wang and eastern longitudes 22° 12’ and 28° 11’. The size of et al. 2007) (see Table 1). All the sequences were the lakes varied from 0.02 to 863.3 km2. According to the deposited in GenBank under accession numbers TP content, the studied lakes ranged from oligotrophic to EU861397–EU861477, FJ970038. hypertrophic. pH values ranged from 5.8 to 10. The pH of Statistical data analysis.