- Email: [email protected]
Eukaryotic and prokaryotic microbial communities during microalgal biomass production
Bioresource Technology 124 (2012) 387–393
Contents lists available at SciVerse ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Eukaryotic and prokaryotic microbial communities during microalgal biomass production Aino-Maija Lakaniemi a,⇑, Chris J. Hulatt b,c, Kathryn D. Wakeman a, David N. Thomas b,c, Jaakko A. Puhakka a a b c
Department of Chemistry and Bioengineering, Tampere University of Technology, P.O. Box 541, FI-33101 Tampere, Finland School of Ocean Sciences, College of Natural Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB, UK Finnish Environment Institute, Marine Centre, P.O. Box 140, FI-00251 Helsinki, Finland
h i g h l i g h t s " Chlorella vulgaris and Dunaliella tertiolecta grow well in the presence of diverse bacteria. " C. vulgaris and D. tertiolecta have different associated bacterial communities. " DGGE detects ciliates before they eradicate the microalgal cultures. " qPCR shows relative microalgal and bacterial cell numbers from stable cultures. " Raw culture samples serve as suitable templates for qPCR.
a r t i c l e
i n f o
Article history: Received 6 June 2012 Received in revised form 6 August 2012 Accepted 11 August 2012 Available online 19 August 2012 Keywords: Microalgal cultivation Photobioreactor Associated bacteria Quantitative PCR DGGE
a b s t r a c t Eukaryotic and bacterial communities were characterized and quantified in microalgal photobioreactor cultures of freshwater Chlorella vulgaris and marine Dunaliella tertiolecta. The microalgae exhibited good growth, whilst both cultures contained diverse bacterial communities. Both cultures included Proteobacteria and Bacteroidetes, while C. vulgaris cultures also contained Actinobacteria. The bacterial genera present in the cultures were different due to different growth medium salinities and possibly different extracellular products. Bacterial community profiles were relatively stable in D. tertiolecta cultures but not in C. vulgaris cultures likely due to presence of ciliates (Colpoda sp.) in the latter. The presence of ciliates did not, however, cause decrease in total number of C. vulgaris or bacteria during 14 days of cultivation. Quantitative PCR (qPCR) reliably showed relative microalgal and bacterial cell numbers in the batch cultures with stable microbial communities, but was not effective when bacterial communities varied. Raw culture samples were successfully used as qPCR templates. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Microalgae are potential feedstocks for fuels and energy due to their high growth rates and photosynthetic efficiencies (Brennan and Owende, 2010). Currently, microalgal biomass is produced commercially for high value products such as human food supplements, animal feed, cosmetics and pharmaceuticals (Gong et al., 2011). However, to date, the industrial-scale production of microalgal biomass for fuels and/or energy remains too costly and energy intensive (Amer et al., 2011; Beal et al., 2012; Hulatt et al., 2012). In nature, microalgal growth is always associated with the growth of other organisms, notably bacteria (Reynolds, 2006).
⇑ Corresponding author. Tel.: +358 40 198 1103; fax: +358 33 115 2869. E-mail address: aino-maija.lakaniemi@tut.fi (A.-M. Lakaniemi). 0960-8524/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2012.08.048
Microalgae provide organic and inorganic compounds for bacteria by excreting soluble material during normal growth, in response to environmental stress and/or via their lysis and decomposition after cell death (Cole, 1982; Reynolds, 2006; Hulatt and Thomas, 2010). Bacteria can supply vitamin B12 for the algae that are unable to synthesize it (Croft et al., 2005), provide CO2 for algal growth, reduce oxygen tension (Mouget et al., 1995), recycle nitrogen compounds (Hulatt et al., 2012) and increase the solubility of nutrients and trace elements making them more bio-available for the microalgae (Keshtacher-Liebson et al., 1995). However, some microalgae also excrete compounds that limit bacterial growth (Stephens et al., 2010). Bacteria may also compete with microalgae for available nutrients, produce metabolites that are inhibitory to microalgal growth, infect microalgae or cause lysis of algal cells (Cole, 1982). Similarly, eukaryotic organisms, such as fungi, may promote microalgal growth by symbiotic associations (Watanabe et al., 2005) or compete with the microalgae for available nutrients.
388
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Other eukaryotes, such as ciliates, may also feed on microalgae (Moreno-Garrido and Canãvate, 2001). In microalgal biomass production, heterotrophic organisms are often considered harmful (Huntley and Redalje, 2007), yet there are reports on the positive effects of bacteria and fungi on algal growth (Watanabe et al., 2005; Park et al., 2008). Sterilization of culture media and growth units in large-scale biomass production for biofuel or bioenergy precursor is neither economically nor practically feasible. Thus, in order to increase biomass production efficiency, a fundamental understanding of the interactions of the different organisms in microalgal growth units is required. Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) has been widely used to qualitatively characterize planktonic communities in natural environments (Díez et al., 2001; Rooney-Varga et al., 2005; Niu et al., 2011), and recently in microalgal photobioreactor (PBR) cultivations (Lakaniemi et al., 2012a,b). Hulatt et al. (2012) described the batch cultivation of two commonly used microalgae in biotechnology applications, the freshwater Chlorella vulgaris and marine Dunaliella tertiolecta, using different nitrogen sources (ammonium, nitrate and urea). Biomass productivity and maximum specific growth rate of D. tertiolecta were similar with all the nitrogen sources. C. vulgaris grew similarly with nitrate and urea, whilst the use of ammonium resulted in a pH decrease followed by culture collapse after 2 days of cultivation. Bacterial numbers in the D. tertiolecta cultures with ammonium were much higher than in the corresponding nitrate and urea cultures. Bacterial growth in C. vulgaris cultures was similar with nitrate and urea, but bacterial growth with ammonium was not measured as C. vulgaris did not grow with ammonium. The aim of this study was to characterize the development of prokaryotic and eukaryotic communities in these open microalgal photobioreactor cultures using PCR-DGGE. Quantitative PCR (qPCR) for enumeration of bacteria and eukaryotes in non-axenic microalgal cultures under different growth conditions was also examined. 2. Methods 2.1. Microalgal cultivation C. vulgaris (CCAP 211/11B) and D. tertiolecta (SAG 13.86) were batch-cultivated for 14 days in polyethene column photobioreactors (PBRs) with a working volume of 20 L, a diameter of 160 mm and temperature of 24 ± 2 °C. Nitrate, urea and ammonium (1.33 mmol L1 N) were used as nitrogen sources. Each cultivation type was performed in triplicate. The PBRs were sparged with air (containing approximately 0.04% CO2) at 10 L min1 and illuminated with eight cool white fluorescent lamps at an incident irradiance (photosynthetically active radiation) of 225 lmol photons m2 s1. The PBR configuration was described in detail by Hulatt and Thomas (2010) and the cultivation conditions by Hulatt et al. (2012). 2.2. Enumeration of algal and bacterial cells Microalgal cells were counted using a haemocytometer. Bacteria were counted using epifluorescence microcopy after staining the cells with 40 ,6-diamidino-2-phenylindole (DAPI) (Hulatt and Thomas, 2010). 2.3. Extraction of DNA from non-axenic microalgal cultures Duplicate samples (19–20 ml) were withdrawn from each type of microalgal cultivation and stored at 20 °C until required. Biomass was harvested by centrifugation (10,000g, 10 min) prior
to DNA extraction of the resultant pellet using the PowerSoil DNA isolation kit (Mo Bio Laboratories) according to the manufacturer’s instructions. 2.4. Microbial community analysis The eukaryotic and bacterial communities in each type of microalgal culture were determined using PCR-DGGE as described by Lakaniemi et al. (2012a). Eukaryotic DGGE was performed on all cultures on days 0 and 14. Bacterial DGGE was performed as a time series (samples taken on days 0, 4, 6, 10 and 14) from one culture of each nitrogen treatment and on days 0 and 4 from other replicate cultures. DNA was amplified from DGGE bands and sequencing of the amplicons was performed by Macrogen Inc. (Seoul, South Korea). Sequence data were edited with BioEdit-software and compared with sequences in GenBank using BLAST. All 16S rRNA gene sequences obtained were submitted to GenBank under accession numbers JQ433976–JQ434000. A phylogenetic tree was constructed from bacterial 16S rRNA gene sequences using the web-based Phylogeny.fr-software (Dereeper et al., 2008). The software utilized MUSCLE 3.7 and Gblocks 0.91b for alignment and PhyML 3.0 aLRT with a maximum likelihood-ratio test for tree construction. An outgroup was not used in tree construction, because typical prokaryotes in microalgal cultures could not be named. 2.5. qPCR Primers specific for the nuclear 18S rRNA gene of eukaryotes, EUK345f and EUK499r and for the 16S rRNA gene of bacteria, 27F and 518R were used for qPCR analyses (Lakaniemi et al., 2012a). Reactions were performed separately for the bacterial and eukaryotic enumerations using triplicate samples for each in a final volume of 20 ll with 10 ll Maxima SYBR Green/ROX qPCR master mix (2) (Fermentas Life Sciences), 0.4 ll of forward and reverse primers and 9.2 ll of template. Thermocycling and monitoring of SYBR Green fluorescence were conducted with a StepOne Plus Real Time PCR machine (Applied Biosystems Inc.) using the following PCR program: 95 °C for 10 min, 30 cycles of 95 °C for 30 s, 55 °C for 30 s and 60 °C for 1 min followed by melting point analysis at 95 °C for 15 s, 60 °C for 1 min and 95 °C for 15 s to demonstrate amplification of a discrete DNA fragment. To verify the consistency of analyses, DNA extracted from diluted (1–100,000 dilutions) day 12 samples of microalgal culture grown on nitrate was used as a template for qPCR. CT was plotted as a function of log10 cell counts to determine if there was a linear relationship between the cycle threshold (CT, the number of PCR cycles required to cross a certain fluorescence threshold) and the cell counts. Amplification efficiencies of the qPCR reaction were calculated from the slope of the curves using Eq. (1).
E ¼ 101=slope 1
ð1Þ
The CT values showed a linear relationship with the microalgal and bacterial (log10) cell counts (Table 1). The relationship between logarithmic cell counts and qPCR results at different dilutions of C. vulgaris and D. tertiolecta cultivation samples (day 12) without DNA extraction was also studied. However, only sporadic 10 and 100 diluted samples were amplified while the other dilutions were not. The qPCR for enumeration of bacteria and eukaryotes in nonaxenic microalgal cultures was further studied in two ways on one of the replicates for each combination of microalgal species and nitrogen source: (i). To test the application of qPCR to different phases of cultivation, DNA extracted from samples (taken once every 2 days) of non-axenic C. vulgaris and D. tertiolecta cultures grown on
389
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Table 1 Equations between CT and cell counts, amplification efficiency and linear range of the qPCR standards generated using DNA extractions of differently diluted samples taken on cultivation day 12 from C. vulgaris and D. tertiolecta cultures grown on NO3. Microalgal culture
Target of qPCR
Standard equation
R2 value
PCR amplification efficiency (%)
Linear range (cells per ml)
Chlorella vulgaris
Eukaryotes Bacteria Eukaryotes Bacteria
CT = 3.72xA + 40.11 CT = 3.53xA + 35.17 CT = 3.44xA + 39.60 CT = 4.09xA + 41.32
0.99 0.99 0.94 0.97
86 92 95 76
8.5 103–8.5 107 9.9 102–9.9 106 5.7 102–5.7 106 2.3 103–2.3 107
Dunaliella tertiolecta
different nitrogen sources was used as a template for bacterial and eukaryotic qPCR. (ii). To test whether DNA extraction was necessary in PCR-based cell enumeration, qPCR was performed on raw samples of C. vulgaris and D. tertiolecta cultures grown on different nitrogen sources.
3. Results and discussion 3.1. Community composition of microalgal cultures Both microalgal species resolved into multiple distinct DGGE bands on eukaryotic DGGE, as previously shown by Lakaniemi et al. (2012a,b). Based on eukaryotic DGGE profiles, the only eukaryote detected in D. tertiolecta cultures on all sampling days was D. tertiolecta (100% similarity to D. tertiolecta, accession number EF537907). The only eukaryote present in C. vulgaris cultures on day 0 was C. vulgaris (100% similarity to C. vulgaris, accession number FN298918). On day 14, a ciliate (100% similarity to Colpoda sp. accession number AY905498) was detected in all the C. vulgaris cultures. Ciliates feed on both microalgae and bacteria and can eradicate even dense microalgal cultures within a few days (Hadas et al., 1998; Moreno-Garrido and Canãvate, 2001). However, no clear decrease in total numbers of microalgae and bacteria after the appearance of the Colpoda sp. was measured (Hulatt et al., 2012). Bacterial DGGE profiles showed diverse bacterial communities in all cultures (Figs. 1 and 2). Selected DGGE bands were amplified and sequenced, but good quality sequence data was not obtained for all of the bands (Figs. 1 and 2, Tables 2 and 3). Different bacteria
were present in C. vulgaris and D. tertiolecta cultures, presumably due to the different salinities of the two growth media (<0.05% and 3%, respectively). However, factors other than salinity, such as abundance and chemical composition of microalgal exudates, may also have affected the bacterial community compositions (Kirchman et al., 2004). Differences in organic and inorganic exudates were likely because C. vulgaris has a rigid cell wall, whereas D. tertiolecta lacks a distinct cell wall. In addition, Schäfer et al. (2002) have shown that microalgal species grown in similar medium salinity support the growth of distinct bacterial communities. Despite the different bacterial community compositions, the final biomass concentration was similar (0.48–0.54 g-dw L1) in cultures of both microalgae (Hulatt et al., 2012). Analysis of sequence data also showed that C. vulgaris chloroplast DNA was amplified with the bacterial PCR primers used, but this was not apparent with D. tertiolecta (Tables 2 and 3). In the C. vulgaris cultures, of the 14 bacterial species, three were assigned to Alphaproteobacteria, two to Actinobacteria, five to Bacteroidetes, two to Betaproteobacteria and two did not match with known bacteria (Table 2, Fig. 3). In the D. tertiolecta cultures, of the 10 bacterial sequences determined; five were assigned to Alphaproteobacteria, four to Bacteroidetes and one to Gammaproteobacteria (Table 3, Fig. 3). These bacterial groups typically associate with freshwater and marine algae, whereas Betaproteobacteria have only been detected in conjunction with freshwater microalgae (Watanabe et al., 2005; Sapp et al., 2007; Otsuka et al., 2008; Lakaniemi et al., 2012a,b). The bacterial community profiles in C. vulgaris cultures of this study were completely different from those in a previous study, where a different C. vulgaris strain (SAG 211-11b) was cultivated
Fig. 1. Bacterial community profiles at different stages of cultivation of C. vulgaris cultures grown on urea (A) and nitrate (B). See Table 2 for the identity of bacteria represented by the labeled bands.
390
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Fig. 2. Bacterial community profiles at different stages of cultivation of D. tertiolecta cultures grown on urea (A), nitrate (B) and ammonium (C). See Table 3 for the identity of bacteria represented by the labeled bands.
Table 2 Identification of bacteria from the C. vulgaris cultures; selected bacterial 16S rDNA based band identities and affiliations.
a b c d
Band label (acc)a
SLb
Sim (%)c
Affiliation (acc)d
C1 (JQ433976) C2 (JQ433977) C3 (JQ433978) C4 (JQ433979) C5 (JQ433980) C6 (JQ433981) C7 (JQ433982) C8 (JQ433983) C9 (JQ433984) C10 (JQ433985) C11 (JQ433986) C12 (JQ433987) C13 (JQ433988) C14 (JQ433989) C15 (JQ433990)
437 439–448 415–441 452–460 477 440–450 420 414 417 348 353 353 365 419 426
98.6–100 89.5–93.4 100 98.7–98.9 99.8 100 97.6 94.9 93.3 95.7–99.4 100 100 92.3 93.8 99.1
Sediminibacterium sp. (JN674641) Uncultured bacterium (GQ388797) Chlorella vulgaris (AB001684) Runella sp. (AB249681) Ralstonia sp. (JN604320) Flavobacterium lindanitolerans (GQ121368) Dyadobacter sp. (GU205378) Mycoplana sp. (EU706018) Uncultured Sphingomonadales bacterium (AM935865) Variovorax sp. (AB622228) Uncultured Leifsonia sp. (FJ542965) Microbacterium sp. (EF028128) Uncultured Pedobacter sp. (EU305584) Uncultured Rhizobiales bacterium (JF727687) Uncultured bacterium (EU136274)
Band label in Fig. 1 with a GenBank accession number. Sequence length. Similarity (%). Closest species in GenBank database with an accession number.
Table 3 Identification of bacteria from the D. tertiolecta cultures; selected bacterial 16S rDNA based band identities and affiliations.
a b c d
Band label (acc)a
SLb
Sim (%)c
Affiliation (acc)d
D1 (JQ433991) D2 (JQ433992) D3 (JQ433993) D4 (JQ433994) D5 (JQ433995) D6 (JQ433996) D7 (JQ433997) D8 (JQ433998) D9 (JQ433999) D10 (JQ434000)
380 426 440 440–449 452 434 449–451 403 422 473
94.2 94.1 91.6 99.8 99.1 96.1 99.8–100 97.5 99.1 100
Flavobacterium sp. (AF386740) Roseobacter sp. (GQ246628) Algoriphagus sp. (EU374905) Roseobacter sp. (DQ659411) Sulfitobacter sp. (AB583769) Ruegeria sp. (FJ984836) Flavobacterium sp. (AF386740) Croceibacter atlanticus (CP002046) Hoeflea sp. (GU564401) Halomonas sp. (EU308361)
Band label in Fig. 2 with a GenBank accession number. Sequence length. Similarity (%). Closest species in GenBank database with an accession number.
in a tap water-based medium (Lakaniemi et al., 2012a), but contained several similar bacteria that were reported in non-axenic Chlorella sp. cultures isolated from soil (Otsuka et al., 2008). D. tertiolecta cultures contained many bacterial genera that have
been also previously reported from non-axenic microalgal cultures (Schäfer et al., 2002; Sapp et al., 2007) and the bacterial communities were partly similar as previously reported for D. tertiolecta (SAG 13.86) cultures grown in tap water-based medium (Lakaniemi et al., 2012b). In the previous studies, the bacteria in C. vulgaris cultures mainly originated from the tap water used in medium preparation (Lakaniemi et al., 2012a), whereas in the tap waterbased D. tertiolecta cultures the high salinity (3%) of the medium inhibited growth of typical tap water bacteria and bacteria originated mainly from non-axenic stock cultures (Lakaniemi et al., 2012b). In this study, Milli-Q-water and not untreated tap water was used in all media. Thus, the bacteria in this study also likely originated from the non-axenic microalgal stock cultures, but may also have included air-borne bacteria from other gas-sparged cultures simultaneously cultivated in the laboratory. This highlights the importance of the careful maintenance of stock cultures and consideration of the water source when controlling associated or introduced bacterial communities. Higher illumination intensity and different initial N concentration compared to previous studies (Lakaniemi et al., 2012b) may also have caused some of the differences seen in the bacterial community compositions.
391
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Alphaproteobacteria
Actinobacteria
Betaproteobacteria
Gammaproteobacteria
Bacteroidetes
Fig. 3. A phylogenetic tree of partial 16S rRNA gene sequences obtained from bacteria present in C. vulgaris and D. tertiolecta cultures with all the tested nitrogen sources. The phylogenetic tree was generated using Phylogeny.fr-software. Only branch support values higher than 50% are shown. Outgroup was not used in tree construction, because typical prokaryotes in microalgal cultures could not be named. The scale bar indicates one substitution per 10 nucleotide positions. Bands with letter C are from C. vulgaris cultures and bands with letter D are from D. tertiolecta cultures. Selected known bacterial species identified as closest matches for the bacteria present in microalgal cultures are shown in grey.
Different nitrogen sources had some effect on bacterial communities (Fig. 1 and 2). For example, band C12 was only present in C. vulgaris cultures grown on urea and C15 only in C. vulgaris cultures grown on nitrate (Fig. 1). Similarly, bands D4 and D9 were only present in D. tertiolecta cultures grown using ammonium and not in cultures using other nitrogen sources (Fig. 2). However, only one batch cultivation cycle was conducted with each of the nitrogen sources and it is possible that differences in bacterial community structures would have been higher if continuous culture mode or repeated sub-culturing with the different nitrogen sources had been tested. Same major bacterial bands were present in D. tertiolecta cultures on day 14 with all tested nitrogen sources. Thus, DGGE results do not explain why end point bacterial numbers were higher in cultures grown on ammonium than in those grown on nitrate or urea. Bacterial community profiles were relatively stable during growth of D. tertiolecta cultures, whilst changes were seen in bacterial communities associated with C. vulgaris cultures, especially when urea was used (Fig. 1 and 2). For instance, on day 6 bacterial DGGE of C. vulgaris cultures grown on urea, contained 11 separate bands, whereas on day 10 the number of bands was 15 and on day 14 only 10 (Fig. 1). Previous studies by Lakaniemi et al. (2012a,b) reported stable bacterial community profiles in cultures of both C. vulgaris and D. tertiolecta. The microalgal cultures were unialgal and contained no other eukaryotes (Lakaniemi et al., 2012a,b). The presence of Colpoda sp. in this study may have caused the day to
day fluctuations in bacterial community profiles of C. vulgaris cultures, although it did not yet reduce the total number of bacteria. Thus, fluctuations in bacterial communities could be taken as a warning sign that the microalgal culture is in danger of being eradicated. PCR-DGGE did not reveal bacterial roles in the microalgal cultures. Although some bacteria, such as Microbacterium (Watanabe et al., 2005) and Brevundimonas spp. (Park et al., 2008), may stimulate microalgal growth in single-member bacterial cultures, the bacterial algal interactions may be different when several different bacteria are present with the microalga as shown by Delucca and McCracken (1977). They reported that Pseudomonas spp. and Flavobacterium spp. enhanced growth of Chlamydomonas when present alone, but when combined the two bacteria inhibited the growth of the alga. 3.2. Enumeration of microalgae and bacteria with qPCR Eukaryotic qPCR results from DNA extraction samples from different phases of cultivation on each nitrogen source were plotted as a function of log10 microalgal cell counts. Linear relationships existed between the two parameters with both C. vulgaris and D. tertiolecta for all conditions tested (Table 4). Linear relationships were also evident for bacterial qPCR results in D. tertiolecta cultures, although this relationship was not apparent for bacteria in C. vulgaris cultures (Table 4). Similarly, when raw samples
392
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Table 4 Trend line equations and R2 values between eukaryotic and bacterial qPCR results and log10 cell counts of microalgae and bacteria, respectively. DNA extracted from culture samples taken at different growth phases of the C. vulgaris and D. tertiolecta cultures were used as templates for qPCR. Eukaryotic qPCR
Chlorella vulgaris
Ammonium Nitrate Urea Ammonium Nitrate Urea
Dunaliella tertiolecta
Bacterial qPCR
Relationship
R2
Relationship
R2
– CT = 3.31xA + 39.35 CT = 4.14xA + 45.83 CT = 3.72xA + 45.07 CT = 3.13xA + 39.29 CT = 2.99xA + 43.01
– 0.81 0.87 0.99 0.97 0.97
– CT = 14.30xA + 107.54 CT = 4.52xA + 45.78 CT = 4.60xA + 52.44 CT = 7.06xA + 68.39 CT = 9.10xA + 82.18
– 0.41 0.17 0.95 0.86 0.99
Table 5 Trend line equations and R2 values obtained between eukaryotic and bacterial qPCR results and logarithms of microalgal and bacterial cell counts, respectively. Raw culture samples taken at different growth phases of C. vulgaris and D. tertiolecta cultures were used as templates for qPCR. Eukaryotic qPCR
Chlorella vulgaris
Ammonium Nitrate Urea Ammonium Nitrate Urea
Dunaliella tertiolecta
R2
Relationship
R2
– CT = 3.03xA + 46.05 CT = 2.20xA + 41.63 CT = 1.01xA + 27.94 CT = 3.14xA + 41.85 CT = 2.75xA + 41.76
– 0.93 0.98 0.77 0.99 0.97
– CT = 17.67xA + 134.11 CT = 3.88xA + 47.63 CT = 2.86xA + 40.37 CT = 6.29xA + 64.00 CT = 7.15xA + 69.37
– 0.61 0.41 0.99 0.78 0.99
B
9 8
Log 10 algal cell count
Log 10 algal cell count
A
Bacterial qPCR
Relationship
7
6 5 4 3
2 1 0
9 8 7
6 5 4 3
2 1 0
0
5
10
15
Time (d)
0
5
10
15
Time (d)
Fig. 4. Comparison of logarithmic cell counts obtained by haemocytometer calculations (d) and logarithmic cell counts calculated using the qPCR results obtained from DNA extraction samples converted into cell counts with qPCR standard curve equations presented in Table 3 (j). These examples were obtained by comparing microalgal cell counts and eukaryotic qPCR results from C. vulgaris cultures (A) and D. tertiolecta cultures (B) grown using nitrate as the nitrogen source.
without DNA extraction were used as template, relationships were linear between eukaryotic qPCR and log10 microalgal cell counts with both microalgal species and between bacterial qPCR results and logarithmic bacterial cell counts in D. tertiolecta cultures but not in C. vulgaris cultures (Table 5). This indicates that DNA extraction is not necessary for qPCR-based cell enumeration, making qPCR more applicable to routine use. However, the sample (template) dilution has to be correct for qPCR to succeed from raw culture samples. Microalgal quantification by qPCR has been conducted successfully also in previous studies (Coyne et al., 2005; Fowler and Wade, 2006; Moorthi et al., 2006; Lakaniemi et al., 2012a,b). However, qPCR measures the number of target genes and not the number of cells. The rDNA copy number may vary between different microbial species as well as within same species at different growth phases (Zhu et al., 2005; Pérez-Osorio et al., 2010). Photoautotrophic microalgal cultures are low-substrate environments compared to heterotrophic cultures. Therefore, the copy number of rDNA can be expected to be relatively small (Klappenbach et al., 2000; Pérez-Osorio et al., 2010). Bacterial community profiles were relatively stable in D. tertiolecta cultures. Also previous studies indicated that qPCR provides good relative estimation of bacterial numbers in microalgal cultures where bacterial community
profiles remain relatively stable (Lakaniemi et al., 2012a,b). Bacterial qPCR results and bacterial cell counts from C. vulgaris cultures of this study did not correlate. This is likely to be due to significant variation of bacterial community profiles. It may have caused variation in cell averaged rDNA copy number and in turn this caused fluctuation in bacterial qPCR results. The relationships between qPCR results and cell counts were different with each different nitrogen source whether sample DNA was extracted or raw samples without DNA extraction were used as templates (Tables 4 and 5). The relationships between qPCR results and log10 cell counts from different cultivation days (Table 4) and those obtained using samples from one cultivation day (Table 1) for C. vulgaris and D. tertiolecta cultures grown on nitrate were different. Small changes in cell averaged rDNA copy numbers during growth may have caused the difference compared to one day samples where such differences were not possible. Bacterial cell counts in C. vulgaris cultures grown on nitrate ranged only between 1.2 106 and 7.3 106 cells ml1 (Hulatt et al., 2012) and this may have also distorted the equation obtained for the relationship of qPCR results and cell counts (Table 4). Therefore, no PCR amplification efficiencies were calculated based on the equations revealing correlations between qPCR results and log10 cell counts for samples taken from different growth phases.
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Fig. 4 shows that the qPCR-based counts were generally lower than those of cell counting. However, trends were similar for eukaryotes in C. vulgaris cultures and for both eukaryotes and bacteria in D. tertiolecta cultures. Thus, the qPCR results clearly showed the trends and the relative amounts of microalgae and bacteria in cultures that had a relatively stable bacterial community composition, but underestimated the actual cell numbers. The statistical significance of the correlations between qPCR results and log10 cell counts were similar for both microalgae and bacteria in D. tertiolecta cultures whether qPCR was conducted from raw culture samples or DNA extractions (Tables 4 and 5). In C. vulgaris cultures the R2 values were somewhat higher when raw culture was used as template for qPCR (Tables 4 and 5). This was not expected as C. vulgaris has a rigid cell wall but D. tertiolecta lacks a distinct cell wall structure. DNA extraction efficiency may vary between samples and DNA extraction batches (Dionisi et al., 2003), and this effect is likely more significant for organisms with more recalcitrant cell wall structures. These results also show that at 10–100 sample dilutions the 3% salinity of D. tertiolecta cultures did not inhibit qPCR reactions. 4. Conclusions C. vulgaris and D. tertiolecta grew well despite the cultures also containing diverse bacterial communities. There was no decrease in C. vulgaris and bacterial numbers when a ciliate Colpoda sp. was detected by DGGE profiling, which highlights the potential of the technique for revealing the presence of grazers before they adversely influence algal cultures. Quantitative PCR reliably measured relative microalgal and bacterial cell numbers in batch cultures with stable microbial communities. The use of raw culture samples as the template for qPCR was validated, although the use of DNA-extracts increased the range of cell numbers that could be determined. Acknowledgements This research was supported by the Finland Distinguished Professor Programs of the Finnish Funding Agency for Technology and Innovation and the Academy of Finland, and an Engineering and Physical Sciences Research Council (EPSRC) Industrial PhD studentship with RWEnpower (UK). We are grateful to Louiza Norman and Naomi Thomas for their analytical support during this study. References Amer, L., Adhikari, B., Pellegrino, J., 2011. Technoeconomic analysis of five microalgae-to-biofuels processes of varying complexity. Bioresour. Technol. 102, 9350–9359. Beal, C.M., Hebner, R.E., Webber, M.E., Ruoff, R.S., Seibert, A.F., 2012. The energy return on investment for algal biocrude: results for a research production facility. Bioenerg. Res. 5, 341–362. Brennan, L., Owende, P., 2010. Biofuels from microalgae – a review of technologies for production, processing, and extractions of biofuels and co-products. Renewable Sustainable Energy Rev. 14, 557–577. Cole, J.J., 1982. Interactions between bacteria and algae in aquatic ecosystems. Annu. Rev. Ecol. Syst. 13, 291–314. Coyne, K.J., Handy, S.M., Demir, E., Whereat, E.B., Hutchins, D.A., Portune, K.J., Doblin, M.A., Cary, S.C., 2005. Improved quantitative real-time PCR assays for enumeration of harmful algal species in field samples using an exogenous DNA reference standard. Limnol. Oceanogr. 3, 381–391. Croft, M.T., Lawrence, A.D., Raux-Deery, E., Warren, M.J., Smith, A.G., 2005. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93. Delucca, R., McCracken, M.D., 1977. Observations on interactions between naturally-collected bacteria and several species of algae. Hydrobiologia 55, 71–75. Dereeper, A., Guignon, V., Blanc, G., Audic, S., Buffet, S., Chevenet, F., Dufayard, J.-F., Guindon, S., Lefort, V., Lescot, M., Claverie, J.-M., Gascuel, O., 2008. Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. 36, W465–W469.
393
Díez, B., Pedrós-Alió, C., Marsh, T., Massana, R., 2001. Application of denaturing gradient gel electrophoresis (DGGE) to study the diversity of marine picoeukaryotic assemblages and comparison of DGGE with other molecular techniques. Appl. Environ. Microbiol. 67, 2942–2951. Dionisi, H.M., Harms, G., Layton, A.C., Gregory, I.R., Parker, J., Hawkins, S.A., Robinson, K.G., Sayler, G.S., 2003. Power analysis for real-time PCR quantification of genes in activated sludge and analysis of the variability introduced by DNA extraction. Appl. Environ. Microbiol. 69, 6597–6604. Fowler, R., Wade, C., 2006. Development of a specific quantitative real-time PCR assay to monitor phytoplankton DNA in Mammoth Cave National Park. Internet document: (accessed June 1st 2012). Gong, Y., Hu, H., Gao, Y., Xu, X., Gao, H., 2011. Microalgae as platforms for production of recombinant proteins and valuable compounds: progress and prospects. J. Ind. Microbiol. Biotechnol. 38, 1879–1890. Hadas, O., Malinsky-Rushansky, N., Pinkas, R., Cappenberg, T.E., 1998. Grazing on autotrophic and heterotrophic picoplankton by ciliates isolated from Lake Kinneret, Israel. J. Plankton Res. 20, 1435–1448. Hulatt, C.J., Lakaniemi, A.-M., Puhakka, J.A., Thomas, D.N., 2012. Energy demands of nitrogen supply in mass cultivation of two commercially important microalgal species, Chlorella vulgaris and Dunaliella tertiolecta. Bioenerg. Res.. http:// dx.doi.org/10.1007/s12155-011-9175-x. Hulatt, C.J., Thomas, D.N., 2010. Dissolved organic matter (DOM) in microalgal photobioreactors: a potential loss in solar energy conversion? Bioresour. Technol. 101, 8690–8697. Huntley, M.E., Redalje, D.G., 2007. CO2 mitigation and renewable oil from photosynthetic microbes: a new appraisal. Mitig. Adapt. Strat. Glob. Change 12, 573–608. Keshtacher-Liebson, E., Hadar, Y., Chen, Y., 1995. Oligotrophic bacteria enhance algal growth under iron-deficient conditions. Appl. Environ. Microbiol. 61, 2439–2441. Kirchman, D.L., Dittel, A.I., Findlay, S.E.G., Fischer, D., 2004. Changes in bacterial activity and community structure in response to dissolved organic matter in the Hudson River, New York. Aquat. Microb. Ecol. 35, 243–257. Klappenbach, J.A., Dunbar, J.M., Schmidt, T.M., 2000. RRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328– 1333. Lakaniemi, A.-M., Intihar, V.M., Tuovinen, O.H., Puhakka, J.A., 2012a. Growth of Chlorella vulgaris and associated bacteria in photobioreactors. Microb. Biotechnol. 5, 69–78. Lakaniemi, A.-M., Intihar, V.M., Tuovinen, O.H., Puhakka, J.A., 2012b. Growth of Dunaliella tertiolecta and associated bacteria in photobioreactors. J. Ind. Microbiol. Biotechnol. 39, 1357–1365. Moorthi, S.D., Countway, P.D., Stauffer, B.A., Caron, D.A., 2006. Use of quantitative real-time PCR to investigate the dynamics of the red tide dinoflagellate Lingulodinium polyedrum. Microb. Ecol. 52, 136–150. Moreno-Garrido, I., Canãvate, J.P., 2001. Assessing chemical compounds for controlling predator ciliates in outdoor mass cultures of green algae Dunaliella salina. Aquacult. Eng. 24, 107–114. Mouget, J.L., Dakhama, A., Lavoie, M.C., de la Noüe, J., 1995. Algal growth enhancement by bacteria: Is consumption of photosynthetic oxygen involved? FEMS Microbiol. Ecol. 18, 35–43. Niu, Y., Shen, H., Chen, J., Xie, P., Yang, X., Tao, M., Ma, Z., Qi, M., 2011. Phytoplankton community succession shaping bacterioplankton community composition in Lake Taihu, China. Water Res. 45, 4169–4182. Otsuka, S., Abe, Y., Fukui, R., Nishiyama, M., Senoo, K., 2008. Presence of previously undescribed bacterial taxa in non-axenic Chlorella cultures. J. Gen. Appl. Microbiol. 54, 187–193. Park, Y., Je, K.W., Lee, K., Jung, S.E., Choi, T.J., 2008. Growth promotion of Chlorella ellipsoidea by co-inoculation with Brevundimonas sp. isolated from the microalga. Hydrobiologia 598, 219–228. Pérez-Osorio, A.C., Williamson, K.S., Franklin, M.J., 2010. Heterogeneous rpoS and rhlR mRNA levels and 16S rRNA/rDNA (rRNA Gene) ratios within Pseudomonas aeruginosa biofilms, sampled by laser capture microdissection. J. Bacteriol. 192, 2991–3000. Reynolds, C.S., 2006. Ecology of phytoplankton. Cambridge University Press, Cambridge, UK. Rooney-Varga, J.N., Giewat, M.W., Savin, M.C., Sood, S., LeGresley, M., Martin, J.L., 2005. Links between phytoplankton and bacterial community dynamics in a coastal marine environment. Microb. Ecol. 49, 163–175. Sapp, M., Schwaderer, A.S., Wiltshire, K.H., Hoppe, H.G., Gerdts, G., Wichels, A., 2007. Species-specific bacterial communities in the pycosphere of microalgae? Microb. Ecol. 53, 683–699. Schäfer, H., Abbas, B., Witte, H., Muyzer, G., 2002. Genetic diversity of ‘satellite’ bacteria present in cultures of marine diatoms. FEMS Microbiol. Ecol. 42, 25–35. Stephens, E., Ross, I.L., Mussgnug, J.H., Wagner, L.D., Borowitzka, M.A., Posten, C., Kruse, O., Hankamer, B., 2010. Future prospects of microalgal biofuel production systems. Trends Plant Sci. 15, 554–564. Watanabe, K., Takihana, N., Aoyagi, H., Hanada, S., Watanabe, Y., Ohmura, N., Saiki, H., Tanaka, H., 2005. Symbiotic association in Chlorella culture. FEMS Microbiol. Ecol. 51, 187–196. Zhu, F., Massana, R., Not, F., Marie, D., Vaulot, D., 2005. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52, 79–92.
Contents lists available at SciVerse ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Eukaryotic and prokaryotic microbial communities during microalgal biomass production Aino-Maija Lakaniemi a,⇑, Chris J. Hulatt b,c, Kathryn D. Wakeman a, David N. Thomas b,c, Jaakko A. Puhakka a a b c
Department of Chemistry and Bioengineering, Tampere University of Technology, P.O. Box 541, FI-33101 Tampere, Finland School of Ocean Sciences, College of Natural Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB, UK Finnish Environment Institute, Marine Centre, P.O. Box 140, FI-00251 Helsinki, Finland
h i g h l i g h t s " Chlorella vulgaris and Dunaliella tertiolecta grow well in the presence of diverse bacteria. " C. vulgaris and D. tertiolecta have different associated bacterial communities. " DGGE detects ciliates before they eradicate the microalgal cultures. " qPCR shows relative microalgal and bacterial cell numbers from stable cultures. " Raw culture samples serve as suitable templates for qPCR.
a r t i c l e
i n f o
Article history: Received 6 June 2012 Received in revised form 6 August 2012 Accepted 11 August 2012 Available online 19 August 2012 Keywords: Microalgal cultivation Photobioreactor Associated bacteria Quantitative PCR DGGE
a b s t r a c t Eukaryotic and bacterial communities were characterized and quantified in microalgal photobioreactor cultures of freshwater Chlorella vulgaris and marine Dunaliella tertiolecta. The microalgae exhibited good growth, whilst both cultures contained diverse bacterial communities. Both cultures included Proteobacteria and Bacteroidetes, while C. vulgaris cultures also contained Actinobacteria. The bacterial genera present in the cultures were different due to different growth medium salinities and possibly different extracellular products. Bacterial community profiles were relatively stable in D. tertiolecta cultures but not in C. vulgaris cultures likely due to presence of ciliates (Colpoda sp.) in the latter. The presence of ciliates did not, however, cause decrease in total number of C. vulgaris or bacteria during 14 days of cultivation. Quantitative PCR (qPCR) reliably showed relative microalgal and bacterial cell numbers in the batch cultures with stable microbial communities, but was not effective when bacterial communities varied. Raw culture samples were successfully used as qPCR templates. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Microalgae are potential feedstocks for fuels and energy due to their high growth rates and photosynthetic efficiencies (Brennan and Owende, 2010). Currently, microalgal biomass is produced commercially for high value products such as human food supplements, animal feed, cosmetics and pharmaceuticals (Gong et al., 2011). However, to date, the industrial-scale production of microalgal biomass for fuels and/or energy remains too costly and energy intensive (Amer et al., 2011; Beal et al., 2012; Hulatt et al., 2012). In nature, microalgal growth is always associated with the growth of other organisms, notably bacteria (Reynolds, 2006).
⇑ Corresponding author. Tel.: +358 40 198 1103; fax: +358 33 115 2869. E-mail address: aino-maija.lakaniemi@tut.fi (A.-M. Lakaniemi). 0960-8524/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2012.08.048
Microalgae provide organic and inorganic compounds for bacteria by excreting soluble material during normal growth, in response to environmental stress and/or via their lysis and decomposition after cell death (Cole, 1982; Reynolds, 2006; Hulatt and Thomas, 2010). Bacteria can supply vitamin B12 for the algae that are unable to synthesize it (Croft et al., 2005), provide CO2 for algal growth, reduce oxygen tension (Mouget et al., 1995), recycle nitrogen compounds (Hulatt et al., 2012) and increase the solubility of nutrients and trace elements making them more bio-available for the microalgae (Keshtacher-Liebson et al., 1995). However, some microalgae also excrete compounds that limit bacterial growth (Stephens et al., 2010). Bacteria may also compete with microalgae for available nutrients, produce metabolites that are inhibitory to microalgal growth, infect microalgae or cause lysis of algal cells (Cole, 1982). Similarly, eukaryotic organisms, such as fungi, may promote microalgal growth by symbiotic associations (Watanabe et al., 2005) or compete with the microalgae for available nutrients.
388
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Other eukaryotes, such as ciliates, may also feed on microalgae (Moreno-Garrido and Canãvate, 2001). In microalgal biomass production, heterotrophic organisms are often considered harmful (Huntley and Redalje, 2007), yet there are reports on the positive effects of bacteria and fungi on algal growth (Watanabe et al., 2005; Park et al., 2008). Sterilization of culture media and growth units in large-scale biomass production for biofuel or bioenergy precursor is neither economically nor practically feasible. Thus, in order to increase biomass production efficiency, a fundamental understanding of the interactions of the different organisms in microalgal growth units is required. Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) has been widely used to qualitatively characterize planktonic communities in natural environments (Díez et al., 2001; Rooney-Varga et al., 2005; Niu et al., 2011), and recently in microalgal photobioreactor (PBR) cultivations (Lakaniemi et al., 2012a,b). Hulatt et al. (2012) described the batch cultivation of two commonly used microalgae in biotechnology applications, the freshwater Chlorella vulgaris and marine Dunaliella tertiolecta, using different nitrogen sources (ammonium, nitrate and urea). Biomass productivity and maximum specific growth rate of D. tertiolecta were similar with all the nitrogen sources. C. vulgaris grew similarly with nitrate and urea, whilst the use of ammonium resulted in a pH decrease followed by culture collapse after 2 days of cultivation. Bacterial numbers in the D. tertiolecta cultures with ammonium were much higher than in the corresponding nitrate and urea cultures. Bacterial growth in C. vulgaris cultures was similar with nitrate and urea, but bacterial growth with ammonium was not measured as C. vulgaris did not grow with ammonium. The aim of this study was to characterize the development of prokaryotic and eukaryotic communities in these open microalgal photobioreactor cultures using PCR-DGGE. Quantitative PCR (qPCR) for enumeration of bacteria and eukaryotes in non-axenic microalgal cultures under different growth conditions was also examined. 2. Methods 2.1. Microalgal cultivation C. vulgaris (CCAP 211/11B) and D. tertiolecta (SAG 13.86) were batch-cultivated for 14 days in polyethene column photobioreactors (PBRs) with a working volume of 20 L, a diameter of 160 mm and temperature of 24 ± 2 °C. Nitrate, urea and ammonium (1.33 mmol L1 N) were used as nitrogen sources. Each cultivation type was performed in triplicate. The PBRs were sparged with air (containing approximately 0.04% CO2) at 10 L min1 and illuminated with eight cool white fluorescent lamps at an incident irradiance (photosynthetically active radiation) of 225 lmol photons m2 s1. The PBR configuration was described in detail by Hulatt and Thomas (2010) and the cultivation conditions by Hulatt et al. (2012). 2.2. Enumeration of algal and bacterial cells Microalgal cells were counted using a haemocytometer. Bacteria were counted using epifluorescence microcopy after staining the cells with 40 ,6-diamidino-2-phenylindole (DAPI) (Hulatt and Thomas, 2010). 2.3. Extraction of DNA from non-axenic microalgal cultures Duplicate samples (19–20 ml) were withdrawn from each type of microalgal cultivation and stored at 20 °C until required. Biomass was harvested by centrifugation (10,000g, 10 min) prior
to DNA extraction of the resultant pellet using the PowerSoil DNA isolation kit (Mo Bio Laboratories) according to the manufacturer’s instructions. 2.4. Microbial community analysis The eukaryotic and bacterial communities in each type of microalgal culture were determined using PCR-DGGE as described by Lakaniemi et al. (2012a). Eukaryotic DGGE was performed on all cultures on days 0 and 14. Bacterial DGGE was performed as a time series (samples taken on days 0, 4, 6, 10 and 14) from one culture of each nitrogen treatment and on days 0 and 4 from other replicate cultures. DNA was amplified from DGGE bands and sequencing of the amplicons was performed by Macrogen Inc. (Seoul, South Korea). Sequence data were edited with BioEdit-software and compared with sequences in GenBank using BLAST. All 16S rRNA gene sequences obtained were submitted to GenBank under accession numbers JQ433976–JQ434000. A phylogenetic tree was constructed from bacterial 16S rRNA gene sequences using the web-based Phylogeny.fr-software (Dereeper et al., 2008). The software utilized MUSCLE 3.7 and Gblocks 0.91b for alignment and PhyML 3.0 aLRT with a maximum likelihood-ratio test for tree construction. An outgroup was not used in tree construction, because typical prokaryotes in microalgal cultures could not be named. 2.5. qPCR Primers specific for the nuclear 18S rRNA gene of eukaryotes, EUK345f and EUK499r and for the 16S rRNA gene of bacteria, 27F and 518R were used for qPCR analyses (Lakaniemi et al., 2012a). Reactions were performed separately for the bacterial and eukaryotic enumerations using triplicate samples for each in a final volume of 20 ll with 10 ll Maxima SYBR Green/ROX qPCR master mix (2) (Fermentas Life Sciences), 0.4 ll of forward and reverse primers and 9.2 ll of template. Thermocycling and monitoring of SYBR Green fluorescence were conducted with a StepOne Plus Real Time PCR machine (Applied Biosystems Inc.) using the following PCR program: 95 °C for 10 min, 30 cycles of 95 °C for 30 s, 55 °C for 30 s and 60 °C for 1 min followed by melting point analysis at 95 °C for 15 s, 60 °C for 1 min and 95 °C for 15 s to demonstrate amplification of a discrete DNA fragment. To verify the consistency of analyses, DNA extracted from diluted (1–100,000 dilutions) day 12 samples of microalgal culture grown on nitrate was used as a template for qPCR. CT was plotted as a function of log10 cell counts to determine if there was a linear relationship between the cycle threshold (CT, the number of PCR cycles required to cross a certain fluorescence threshold) and the cell counts. Amplification efficiencies of the qPCR reaction were calculated from the slope of the curves using Eq. (1).
E ¼ 101=slope 1
ð1Þ
The CT values showed a linear relationship with the microalgal and bacterial (log10) cell counts (Table 1). The relationship between logarithmic cell counts and qPCR results at different dilutions of C. vulgaris and D. tertiolecta cultivation samples (day 12) without DNA extraction was also studied. However, only sporadic 10 and 100 diluted samples were amplified while the other dilutions were not. The qPCR for enumeration of bacteria and eukaryotes in nonaxenic microalgal cultures was further studied in two ways on one of the replicates for each combination of microalgal species and nitrogen source: (i). To test the application of qPCR to different phases of cultivation, DNA extracted from samples (taken once every 2 days) of non-axenic C. vulgaris and D. tertiolecta cultures grown on
389
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Table 1 Equations between CT and cell counts, amplification efficiency and linear range of the qPCR standards generated using DNA extractions of differently diluted samples taken on cultivation day 12 from C. vulgaris and D. tertiolecta cultures grown on NO3. Microalgal culture
Target of qPCR
Standard equation
R2 value
PCR amplification efficiency (%)
Linear range (cells per ml)
Chlorella vulgaris
Eukaryotes Bacteria Eukaryotes Bacteria
CT = 3.72xA + 40.11 CT = 3.53xA + 35.17 CT = 3.44xA + 39.60 CT = 4.09xA + 41.32
0.99 0.99 0.94 0.97
86 92 95 76
8.5 103–8.5 107 9.9 102–9.9 106 5.7 102–5.7 106 2.3 103–2.3 107
Dunaliella tertiolecta
different nitrogen sources was used as a template for bacterial and eukaryotic qPCR. (ii). To test whether DNA extraction was necessary in PCR-based cell enumeration, qPCR was performed on raw samples of C. vulgaris and D. tertiolecta cultures grown on different nitrogen sources.
3. Results and discussion 3.1. Community composition of microalgal cultures Both microalgal species resolved into multiple distinct DGGE bands on eukaryotic DGGE, as previously shown by Lakaniemi et al. (2012a,b). Based on eukaryotic DGGE profiles, the only eukaryote detected in D. tertiolecta cultures on all sampling days was D. tertiolecta (100% similarity to D. tertiolecta, accession number EF537907). The only eukaryote present in C. vulgaris cultures on day 0 was C. vulgaris (100% similarity to C. vulgaris, accession number FN298918). On day 14, a ciliate (100% similarity to Colpoda sp. accession number AY905498) was detected in all the C. vulgaris cultures. Ciliates feed on both microalgae and bacteria and can eradicate even dense microalgal cultures within a few days (Hadas et al., 1998; Moreno-Garrido and Canãvate, 2001). However, no clear decrease in total numbers of microalgae and bacteria after the appearance of the Colpoda sp. was measured (Hulatt et al., 2012). Bacterial DGGE profiles showed diverse bacterial communities in all cultures (Figs. 1 and 2). Selected DGGE bands were amplified and sequenced, but good quality sequence data was not obtained for all of the bands (Figs. 1 and 2, Tables 2 and 3). Different bacteria
were present in C. vulgaris and D. tertiolecta cultures, presumably due to the different salinities of the two growth media (<0.05% and 3%, respectively). However, factors other than salinity, such as abundance and chemical composition of microalgal exudates, may also have affected the bacterial community compositions (Kirchman et al., 2004). Differences in organic and inorganic exudates were likely because C. vulgaris has a rigid cell wall, whereas D. tertiolecta lacks a distinct cell wall. In addition, Schäfer et al. (2002) have shown that microalgal species grown in similar medium salinity support the growth of distinct bacterial communities. Despite the different bacterial community compositions, the final biomass concentration was similar (0.48–0.54 g-dw L1) in cultures of both microalgae (Hulatt et al., 2012). Analysis of sequence data also showed that C. vulgaris chloroplast DNA was amplified with the bacterial PCR primers used, but this was not apparent with D. tertiolecta (Tables 2 and 3). In the C. vulgaris cultures, of the 14 bacterial species, three were assigned to Alphaproteobacteria, two to Actinobacteria, five to Bacteroidetes, two to Betaproteobacteria and two did not match with known bacteria (Table 2, Fig. 3). In the D. tertiolecta cultures, of the 10 bacterial sequences determined; five were assigned to Alphaproteobacteria, four to Bacteroidetes and one to Gammaproteobacteria (Table 3, Fig. 3). These bacterial groups typically associate with freshwater and marine algae, whereas Betaproteobacteria have only been detected in conjunction with freshwater microalgae (Watanabe et al., 2005; Sapp et al., 2007; Otsuka et al., 2008; Lakaniemi et al., 2012a,b). The bacterial community profiles in C. vulgaris cultures of this study were completely different from those in a previous study, where a different C. vulgaris strain (SAG 211-11b) was cultivated
Fig. 1. Bacterial community profiles at different stages of cultivation of C. vulgaris cultures grown on urea (A) and nitrate (B). See Table 2 for the identity of bacteria represented by the labeled bands.
390
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Fig. 2. Bacterial community profiles at different stages of cultivation of D. tertiolecta cultures grown on urea (A), nitrate (B) and ammonium (C). See Table 3 for the identity of bacteria represented by the labeled bands.
Table 2 Identification of bacteria from the C. vulgaris cultures; selected bacterial 16S rDNA based band identities and affiliations.
a b c d
Band label (acc)a
SLb
Sim (%)c
Affiliation (acc)d
C1 (JQ433976) C2 (JQ433977) C3 (JQ433978) C4 (JQ433979) C5 (JQ433980) C6 (JQ433981) C7 (JQ433982) C8 (JQ433983) C9 (JQ433984) C10 (JQ433985) C11 (JQ433986) C12 (JQ433987) C13 (JQ433988) C14 (JQ433989) C15 (JQ433990)
437 439–448 415–441 452–460 477 440–450 420 414 417 348 353 353 365 419 426
98.6–100 89.5–93.4 100 98.7–98.9 99.8 100 97.6 94.9 93.3 95.7–99.4 100 100 92.3 93.8 99.1
Sediminibacterium sp. (JN674641) Uncultured bacterium (GQ388797) Chlorella vulgaris (AB001684) Runella sp. (AB249681) Ralstonia sp. (JN604320) Flavobacterium lindanitolerans (GQ121368) Dyadobacter sp. (GU205378) Mycoplana sp. (EU706018) Uncultured Sphingomonadales bacterium (AM935865) Variovorax sp. (AB622228) Uncultured Leifsonia sp. (FJ542965) Microbacterium sp. (EF028128) Uncultured Pedobacter sp. (EU305584) Uncultured Rhizobiales bacterium (JF727687) Uncultured bacterium (EU136274)
Band label in Fig. 1 with a GenBank accession number. Sequence length. Similarity (%). Closest species in GenBank database with an accession number.
Table 3 Identification of bacteria from the D. tertiolecta cultures; selected bacterial 16S rDNA based band identities and affiliations.
a b c d
Band label (acc)a
SLb
Sim (%)c
Affiliation (acc)d
D1 (JQ433991) D2 (JQ433992) D3 (JQ433993) D4 (JQ433994) D5 (JQ433995) D6 (JQ433996) D7 (JQ433997) D8 (JQ433998) D9 (JQ433999) D10 (JQ434000)
380 426 440 440–449 452 434 449–451 403 422 473
94.2 94.1 91.6 99.8 99.1 96.1 99.8–100 97.5 99.1 100
Flavobacterium sp. (AF386740) Roseobacter sp. (GQ246628) Algoriphagus sp. (EU374905) Roseobacter sp. (DQ659411) Sulfitobacter sp. (AB583769) Ruegeria sp. (FJ984836) Flavobacterium sp. (AF386740) Croceibacter atlanticus (CP002046) Hoeflea sp. (GU564401) Halomonas sp. (EU308361)
Band label in Fig. 2 with a GenBank accession number. Sequence length. Similarity (%). Closest species in GenBank database with an accession number.
in a tap water-based medium (Lakaniemi et al., 2012a), but contained several similar bacteria that were reported in non-axenic Chlorella sp. cultures isolated from soil (Otsuka et al., 2008). D. tertiolecta cultures contained many bacterial genera that have
been also previously reported from non-axenic microalgal cultures (Schäfer et al., 2002; Sapp et al., 2007) and the bacterial communities were partly similar as previously reported for D. tertiolecta (SAG 13.86) cultures grown in tap water-based medium (Lakaniemi et al., 2012b). In the previous studies, the bacteria in C. vulgaris cultures mainly originated from the tap water used in medium preparation (Lakaniemi et al., 2012a), whereas in the tap waterbased D. tertiolecta cultures the high salinity (3%) of the medium inhibited growth of typical tap water bacteria and bacteria originated mainly from non-axenic stock cultures (Lakaniemi et al., 2012b). In this study, Milli-Q-water and not untreated tap water was used in all media. Thus, the bacteria in this study also likely originated from the non-axenic microalgal stock cultures, but may also have included air-borne bacteria from other gas-sparged cultures simultaneously cultivated in the laboratory. This highlights the importance of the careful maintenance of stock cultures and consideration of the water source when controlling associated or introduced bacterial communities. Higher illumination intensity and different initial N concentration compared to previous studies (Lakaniemi et al., 2012b) may also have caused some of the differences seen in the bacterial community compositions.
391
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Alphaproteobacteria
Actinobacteria
Betaproteobacteria
Gammaproteobacteria
Bacteroidetes
Fig. 3. A phylogenetic tree of partial 16S rRNA gene sequences obtained from bacteria present in C. vulgaris and D. tertiolecta cultures with all the tested nitrogen sources. The phylogenetic tree was generated using Phylogeny.fr-software. Only branch support values higher than 50% are shown. Outgroup was not used in tree construction, because typical prokaryotes in microalgal cultures could not be named. The scale bar indicates one substitution per 10 nucleotide positions. Bands with letter C are from C. vulgaris cultures and bands with letter D are from D. tertiolecta cultures. Selected known bacterial species identified as closest matches for the bacteria present in microalgal cultures are shown in grey.
Different nitrogen sources had some effect on bacterial communities (Fig. 1 and 2). For example, band C12 was only present in C. vulgaris cultures grown on urea and C15 only in C. vulgaris cultures grown on nitrate (Fig. 1). Similarly, bands D4 and D9 were only present in D. tertiolecta cultures grown using ammonium and not in cultures using other nitrogen sources (Fig. 2). However, only one batch cultivation cycle was conducted with each of the nitrogen sources and it is possible that differences in bacterial community structures would have been higher if continuous culture mode or repeated sub-culturing with the different nitrogen sources had been tested. Same major bacterial bands were present in D. tertiolecta cultures on day 14 with all tested nitrogen sources. Thus, DGGE results do not explain why end point bacterial numbers were higher in cultures grown on ammonium than in those grown on nitrate or urea. Bacterial community profiles were relatively stable during growth of D. tertiolecta cultures, whilst changes were seen in bacterial communities associated with C. vulgaris cultures, especially when urea was used (Fig. 1 and 2). For instance, on day 6 bacterial DGGE of C. vulgaris cultures grown on urea, contained 11 separate bands, whereas on day 10 the number of bands was 15 and on day 14 only 10 (Fig. 1). Previous studies by Lakaniemi et al. (2012a,b) reported stable bacterial community profiles in cultures of both C. vulgaris and D. tertiolecta. The microalgal cultures were unialgal and contained no other eukaryotes (Lakaniemi et al., 2012a,b). The presence of Colpoda sp. in this study may have caused the day to
day fluctuations in bacterial community profiles of C. vulgaris cultures, although it did not yet reduce the total number of bacteria. Thus, fluctuations in bacterial communities could be taken as a warning sign that the microalgal culture is in danger of being eradicated. PCR-DGGE did not reveal bacterial roles in the microalgal cultures. Although some bacteria, such as Microbacterium (Watanabe et al., 2005) and Brevundimonas spp. (Park et al., 2008), may stimulate microalgal growth in single-member bacterial cultures, the bacterial algal interactions may be different when several different bacteria are present with the microalga as shown by Delucca and McCracken (1977). They reported that Pseudomonas spp. and Flavobacterium spp. enhanced growth of Chlamydomonas when present alone, but when combined the two bacteria inhibited the growth of the alga. 3.2. Enumeration of microalgae and bacteria with qPCR Eukaryotic qPCR results from DNA extraction samples from different phases of cultivation on each nitrogen source were plotted as a function of log10 microalgal cell counts. Linear relationships existed between the two parameters with both C. vulgaris and D. tertiolecta for all conditions tested (Table 4). Linear relationships were also evident for bacterial qPCR results in D. tertiolecta cultures, although this relationship was not apparent for bacteria in C. vulgaris cultures (Table 4). Similarly, when raw samples
392
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Table 4 Trend line equations and R2 values between eukaryotic and bacterial qPCR results and log10 cell counts of microalgae and bacteria, respectively. DNA extracted from culture samples taken at different growth phases of the C. vulgaris and D. tertiolecta cultures were used as templates for qPCR. Eukaryotic qPCR
Chlorella vulgaris
Ammonium Nitrate Urea Ammonium Nitrate Urea
Dunaliella tertiolecta
Bacterial qPCR
Relationship
R2
Relationship
R2
– CT = 3.31xA + 39.35 CT = 4.14xA + 45.83 CT = 3.72xA + 45.07 CT = 3.13xA + 39.29 CT = 2.99xA + 43.01
– 0.81 0.87 0.99 0.97 0.97
– CT = 14.30xA + 107.54 CT = 4.52xA + 45.78 CT = 4.60xA + 52.44 CT = 7.06xA + 68.39 CT = 9.10xA + 82.18
– 0.41 0.17 0.95 0.86 0.99
Table 5 Trend line equations and R2 values obtained between eukaryotic and bacterial qPCR results and logarithms of microalgal and bacterial cell counts, respectively. Raw culture samples taken at different growth phases of C. vulgaris and D. tertiolecta cultures were used as templates for qPCR. Eukaryotic qPCR
Chlorella vulgaris
Ammonium Nitrate Urea Ammonium Nitrate Urea
Dunaliella tertiolecta
R2
Relationship
R2
– CT = 3.03xA + 46.05 CT = 2.20xA + 41.63 CT = 1.01xA + 27.94 CT = 3.14xA + 41.85 CT = 2.75xA + 41.76
– 0.93 0.98 0.77 0.99 0.97
– CT = 17.67xA + 134.11 CT = 3.88xA + 47.63 CT = 2.86xA + 40.37 CT = 6.29xA + 64.00 CT = 7.15xA + 69.37
– 0.61 0.41 0.99 0.78 0.99
B
9 8
Log 10 algal cell count
Log 10 algal cell count
A
Bacterial qPCR
Relationship
7
6 5 4 3
2 1 0
9 8 7
6 5 4 3
2 1 0
0
5
10
15
Time (d)
0
5
10
15
Time (d)
Fig. 4. Comparison of logarithmic cell counts obtained by haemocytometer calculations (d) and logarithmic cell counts calculated using the qPCR results obtained from DNA extraction samples converted into cell counts with qPCR standard curve equations presented in Table 3 (j). These examples were obtained by comparing microalgal cell counts and eukaryotic qPCR results from C. vulgaris cultures (A) and D. tertiolecta cultures (B) grown using nitrate as the nitrogen source.
without DNA extraction were used as template, relationships were linear between eukaryotic qPCR and log10 microalgal cell counts with both microalgal species and between bacterial qPCR results and logarithmic bacterial cell counts in D. tertiolecta cultures but not in C. vulgaris cultures (Table 5). This indicates that DNA extraction is not necessary for qPCR-based cell enumeration, making qPCR more applicable to routine use. However, the sample (template) dilution has to be correct for qPCR to succeed from raw culture samples. Microalgal quantification by qPCR has been conducted successfully also in previous studies (Coyne et al., 2005; Fowler and Wade, 2006; Moorthi et al., 2006; Lakaniemi et al., 2012a,b). However, qPCR measures the number of target genes and not the number of cells. The rDNA copy number may vary between different microbial species as well as within same species at different growth phases (Zhu et al., 2005; Pérez-Osorio et al., 2010). Photoautotrophic microalgal cultures are low-substrate environments compared to heterotrophic cultures. Therefore, the copy number of rDNA can be expected to be relatively small (Klappenbach et al., 2000; Pérez-Osorio et al., 2010). Bacterial community profiles were relatively stable in D. tertiolecta cultures. Also previous studies indicated that qPCR provides good relative estimation of bacterial numbers in microalgal cultures where bacterial community
profiles remain relatively stable (Lakaniemi et al., 2012a,b). Bacterial qPCR results and bacterial cell counts from C. vulgaris cultures of this study did not correlate. This is likely to be due to significant variation of bacterial community profiles. It may have caused variation in cell averaged rDNA copy number and in turn this caused fluctuation in bacterial qPCR results. The relationships between qPCR results and cell counts were different with each different nitrogen source whether sample DNA was extracted or raw samples without DNA extraction were used as templates (Tables 4 and 5). The relationships between qPCR results and log10 cell counts from different cultivation days (Table 4) and those obtained using samples from one cultivation day (Table 1) for C. vulgaris and D. tertiolecta cultures grown on nitrate were different. Small changes in cell averaged rDNA copy numbers during growth may have caused the difference compared to one day samples where such differences were not possible. Bacterial cell counts in C. vulgaris cultures grown on nitrate ranged only between 1.2 106 and 7.3 106 cells ml1 (Hulatt et al., 2012) and this may have also distorted the equation obtained for the relationship of qPCR results and cell counts (Table 4). Therefore, no PCR amplification efficiencies were calculated based on the equations revealing correlations between qPCR results and log10 cell counts for samples taken from different growth phases.
A.-M. Lakaniemi et al. / Bioresource Technology 124 (2012) 387–393
Fig. 4 shows that the qPCR-based counts were generally lower than those of cell counting. However, trends were similar for eukaryotes in C. vulgaris cultures and for both eukaryotes and bacteria in D. tertiolecta cultures. Thus, the qPCR results clearly showed the trends and the relative amounts of microalgae and bacteria in cultures that had a relatively stable bacterial community composition, but underestimated the actual cell numbers. The statistical significance of the correlations between qPCR results and log10 cell counts were similar for both microalgae and bacteria in D. tertiolecta cultures whether qPCR was conducted from raw culture samples or DNA extractions (Tables 4 and 5). In C. vulgaris cultures the R2 values were somewhat higher when raw culture was used as template for qPCR (Tables 4 and 5). This was not expected as C. vulgaris has a rigid cell wall but D. tertiolecta lacks a distinct cell wall structure. DNA extraction efficiency may vary between samples and DNA extraction batches (Dionisi et al., 2003), and this effect is likely more significant for organisms with more recalcitrant cell wall structures. These results also show that at 10–100 sample dilutions the 3% salinity of D. tertiolecta cultures did not inhibit qPCR reactions. 4. Conclusions C. vulgaris and D. tertiolecta grew well despite the cultures also containing diverse bacterial communities. There was no decrease in C. vulgaris and bacterial numbers when a ciliate Colpoda sp. was detected by DGGE profiling, which highlights the potential of the technique for revealing the presence of grazers before they adversely influence algal cultures. Quantitative PCR reliably measured relative microalgal and bacterial cell numbers in batch cultures with stable microbial communities. The use of raw culture samples as the template for qPCR was validated, although the use of DNA-extracts increased the range of cell numbers that could be determined. Acknowledgements This research was supported by the Finland Distinguished Professor Programs of the Finnish Funding Agency for Technology and Innovation and the Academy of Finland, and an Engineering and Physical Sciences Research Council (EPSRC) Industrial PhD studentship with RWEnpower (UK). We are grateful to Louiza Norman and Naomi Thomas for their analytical support during this study. References Amer, L., Adhikari, B., Pellegrino, J., 2011. Technoeconomic analysis of five microalgae-to-biofuels processes of varying complexity. Bioresour. Technol. 102, 9350–9359. Beal, C.M., Hebner, R.E., Webber, M.E., Ruoff, R.S., Seibert, A.F., 2012. The energy return on investment for algal biocrude: results for a research production facility. Bioenerg. Res. 5, 341–362. Brennan, L., Owende, P., 2010. Biofuels from microalgae – a review of technologies for production, processing, and extractions of biofuels and co-products. Renewable Sustainable Energy Rev. 14, 557–577. Cole, J.J., 1982. Interactions between bacteria and algae in aquatic ecosystems. Annu. Rev. Ecol. Syst. 13, 291–314. Coyne, K.J., Handy, S.M., Demir, E., Whereat, E.B., Hutchins, D.A., Portune, K.J., Doblin, M.A., Cary, S.C., 2005. Improved quantitative real-time PCR assays for enumeration of harmful algal species in field samples using an exogenous DNA reference standard. Limnol. Oceanogr. 3, 381–391. Croft, M.T., Lawrence, A.D., Raux-Deery, E., Warren, M.J., Smith, A.G., 2005. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93. Delucca, R., McCracken, M.D., 1977. Observations on interactions between naturally-collected bacteria and several species of algae. Hydrobiologia 55, 71–75. Dereeper, A., Guignon, V., Blanc, G., Audic, S., Buffet, S., Chevenet, F., Dufayard, J.-F., Guindon, S., Lefort, V., Lescot, M., Claverie, J.-M., Gascuel, O., 2008. Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. 36, W465–W469.
393
Díez, B., Pedrós-Alió, C., Marsh, T., Massana, R., 2001. Application of denaturing gradient gel electrophoresis (DGGE) to study the diversity of marine picoeukaryotic assemblages and comparison of DGGE with other molecular techniques. Appl. Environ. Microbiol. 67, 2942–2951. Dionisi, H.M., Harms, G., Layton, A.C., Gregory, I.R., Parker, J., Hawkins, S.A., Robinson, K.G., Sayler, G.S., 2003. Power analysis for real-time PCR quantification of genes in activated sludge and analysis of the variability introduced by DNA extraction. Appl. Environ. Microbiol. 69, 6597–6604. Fowler, R., Wade, C., 2006. Development of a specific quantitative real-time PCR assay to monitor phytoplankton DNA in Mammoth Cave National Park. Internet document: