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Combined microbial community level and single species biosensor responses to monitor recovery of oil polluted soil
Soil Biology & Biochemistry 36 (2004) 1149–1159 www.elsevier.com/locate/soilbio
Combined microbial community level and single species biosensor responses to monitor recovery of oil polluted soil Jacob G. Bundya,b,1, Graeme I. Patonb, Colin D. Campbella,* b
a The Macaulay Institute, Craigiebuckler, Aberdeen AB15 8QH, UK Department of Plant and Soil Science, University of Aberdeen, Cruickshank Building, St Machar Drive, Aberdeen AB24 3UU, UK
Received 24 September 2002; received in revised form 11 February 2004; accepted 24 February 2004
Abstract Understanding the effects of oil contamination on the composition and function of soil microbiota entails investigation of the effects of a mixture of hydrocarbons at the community level in a complex environmental matrix. One approach to this difficult problem is to ally a community-level fingerprinting approach with bioassays that have a physiological or functional implication. Two contrasting refined oils (paraffin and motor oil) were used to contaminate soil microcosms, and a simulated bioremediation treatment with nutrient-addition was applied. The indigenous microorganisms were monitored over 103 d using complementary community-level techniques (carbon source physiological profiling using Biologw microplates, and phospholipid fatty acid (PLFA) profiling). Changes in the toxicity of the applied oils were monitored using luminescent bacterial bioassays, including Vibrio fischeri and a hydrocarbon-degrading Pseudomonas putida strain. Distinct shifts in microbial community structure and C source utilization profiles were observed as a result of oil contamination. There was some evidence that bioremediated soils were returning to control values by the end of the experiment. This was supported by the bioassay results which showed an initial increase in toxicity as a result of the oil addition which had then decreased by the conclusion of the experiment. The two oils exhibited markedly different toxicity towards the bioassay organisms, with species-specific differences in response. This oil-specific difference was also found in the PLFA profiles which showed the two oil types selected different microbial communities. q 2004 Elsevier Ltd. All rights reserved. Keywords: Oil contamination; Bioremediation; Carbon substrate utilization; Phospholipid fatty acid analysis; Community structure
1. Introduction Soil contamination by petroleum products is a widespread problem, with many hotspots of pollution arising from individual spills (Whittaker et al., 1995). Cleanup of these contaminated sites is an important goal, and bioremediation is a low-input and cheap approach to remove hydrocarbons. Many related approaches can be described as bioremediation, including active techniques such as addition of nutrients or surfactants, or alternatively passive techniques such as natural attenuation, for each of which it is critically important to monitor the progress of oil disappearance. The current study focuses on nutrientassisted bioremediation, which relies on the natural soil * Corresponding author. Tel.: þ44-1224-498-200; fax: þ 44-1224-498207. E-mail address: [email protected] (C.D. Campbell). 1 Present address: Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 0038-0717/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2004.02.025
community but adds exogenous sources of N and P to increase the rate of hydrocarbon degradation. If bioremediation is the strategy of choice, accurate and reliable methods for following the course of biodegradation are essential. Simple residue concentration-based targets, such as a maximum allowable amount of total petroleum hydrocarbons (TPH), are common, but have been criticized on the grounds that this conveys nothing about the differing chemical composition of different oils, which will have different toxicities, degradability, and breakdown products (Michelsen and Boyce, 1993; McMillen et al., 1995). It has been suggested as an alternative that the demonstration of mitigation of biological effects, or reduction of risk, could form the basis of a more biologically relevant endpoint (Salanitro et al., 1997; White et al., 1998; Stroo et al., 2000). Earthworm and other invertebrate, microbial and plant bioassays (Salanitro et al., 1997; Saterbak et al., 1999; van Gestel et al., 2001) have all been studied. However, given the importance of understanding soil microbial ecology to
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bioremediation (Holden and Firestone, 1997), it seems logical that the effects on the indigenous microbiota should also be determined. As the soil microbial community plays a major role in soil functioning, in particular C turnover and other nutrient transformations, it is therefore important to understand any potential negative effects of oil pollution. Two broad approaches can be defined for investigating contaminant effects on microbes. Firstly, effects on the entire community can be measured using a community-level multivariate (profiling) technique. In practice, no single technique currently in use reports on the entire soil microbial community (Madsen, 1998; Ogram, 2000; Kozdro´j and van Elsas, 2001). Culture-based methods measure only a small fraction of the community; soil DNA profiling methods, such as PCR followed by denaturing gradient gel electrophoresis (DGGE), may be restricted by selection of primers, and PCR bias is also likely to affect the results obtained. Biases caused by non-quantitative extraction of nucleic acids from soil are also likely (Martin-Laurent et al., 2001). Similarly, nonDNA based molecular methods such as phospholipid fatty acid (PLFA) profiling do not report on the Archaea (Zelles, 1999). It is therefore sensible to use a combination of techniques. It has been suggested that microbial community response could form the basis of a measure of remediation success by demonstrating that the community response has followed a trajectory through multivariate dimensions, from control to adversely affected, and then returns to control profiles (White et al., 1998). The second approach is to look more closely at the properties or mechanistic effects of a stress event (such as oil contamination) by employing single-species microbial tests and bioassays. Studies using the luminescent bacteria test (Vibrio fischeri, Microtox) have shown that bioremediation causes an initial increase in toxicity followed by a return to control levels (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). However, there are drawbacks to using a marine organism such as V. fischeri for terrestrial ecotoxicity assessment (Paton et al., 1997). Consequently, genetically modified (lux-marked) bioluminescent bacteria, using more ecologically-relevant species, have been used in toxicity bioassays to assess oil bioremediation (Bundy et al., 2001). There are likely to be advantages to combining the community-level approach with complementary singlespecies toxicity bioassays to monitor the effects of refined oil contamination and bioremediation on soil microorganisms because the community response gives an indication of medium- and long-term effects, whereas the bioassays can report on the potential acute toxic response to pollutants (Brohon et al., 2001). Hence, comparison of community profiling techniques with toxicity response data obtained from single organisms may lead to a better understanding of the toxic effects of oil contamination. Our aim was to compare community-level fingerprinting techniques (PLFA profiling and C substrate utilization profiling) with single-species microbial toxicity bioassays,
in an attempt to provide complementary information on the response of soil microcosms contaminated with light-refined paraffin or motor oil, and how the response changes with time. The specific hypotheses we addressed were: (a) that different oil types, light and heavy, would select different soil microbial communities; and (b) that ecological recovery after oil contamination will be manifested at the community level by a return to original constitution of microbial profiles, and at the physiological level by a reduction in the toxicity of the oil and its initial degradation products towards the singlespecies bioassays. The response of the marine bacterium V. fischeri, as a standard bioassay organism, was compared to a more environmentally-relevant hydrocarbon-degrading soil organism, Pseudomonas putida.
2. Materials and methods 2.1. Oil and soil properties Soil (cambisol; Insch series, Insch association) was taken from a grassland site with no history of petroleum contamination. Soil was collected from the A horizon, sieved (5 mm) field-moist through a stainless-steel sieve, and stored at 4 8C until use. The soil was stored for less than 1 month. The soil had a sandy loam texture, an organic C content of 2.1%, and a pH (CaCl2, 10 mM) of 5.9. Standard commercial-grade motor oil and paraffin were purchased from a local supplier and used without modification. 2.2. Experimental design Approximately 60 g wet weight of soil was placed without packing into a 250 ml glass jar and the jar sealed with stretched parafilm. The jars were kept at 25 8C in the dark at 90% relative humidity. Triplicate sets of jars were contaminated with either paraffin, motor oil or left as controls. All microcosms including the controls were treated with nutrient solution (see below). Triplicate microcosms were destructively sampled at each sampling time. There were four sampling dates, hence 36 microcosms in total were used. Microcosms were arranged in a random order, and rearranged every 2 –3 weeks throughout the experiment to compensate for any local temperature fluctuations in the incubator. The microcosms were sampled at 1, 27, 62, and 103 days. Upon sampling, the soil within each jar was mixed and portions of soil were then used for Biolog communitylevel physiological profiling (CLPP, 10 g soil), PLFA analysis (10 g), respiration and water content (20 g), pH (5 g), TPH (10 g), and bioassay analysis (2 g). 2.3. Oil and nutrient addition Moist soil, at approximately 40% of water holding capacity (WHC), was spiked by manual mixing with the paraffin or motor oil. The soil was spread thinly and evenly
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in a large glass dish; the oil was poured evenly over the surface, and then mixed with a stainless steel spatula for 5 min. Oil was added at the rate of 20 g kg21 dry weight of soil (2%). This concentration is typical of bioremediation studies and is well within the reported range of environmental contamination by petroleum products (Whittaker et al., 1995). Nutrient solution was then poured evenly over the soil in order to simulate a bioremediation treatment. Additional water was added to bring the final moisture content of each soil to 55% of WHC. The soil was then mixed for a further 5 min. The nutrient solution contained NH4NO3 as the N source, and P as a K2HPO4/KH2PO4 buffer solution with a pH of 6.4. The final solution contained 28.0 g N l21 and 3.10 g P l21, resulting in a final oil:N:P ratio of 100:10:1 (approximate C:N:P ratio 100:12:1.25). 2.4. Respiration Basal respiration was measured by monitoring CO2 evolution using a GC (Perkin Elmer F33, Cambridge, UK) equipped with a packed column and thermal conductivity detector. Concentrations were calculated relative to a CO2 standard mix of 0.13% (v/v). Soil was taken from the jar upon sampling and placed into a fresh sealed container, and the container then incubated at 25 8C for a further 6 h. CO2 concentrations were then sampled every 2 h for this 6 h period, and the rate of CO2 increase calculated. 2.5. Biolog community-level physiological profiling Soil (10 g wet weight) was added to 100 ml of deionized water in 250 ml conical flasks and shaken for 10 min on a wrist-action shaker. The resulting aqueous extract was used for the basis of a 10-fold dilution series. The 1024 dilution was then centrifuged at 100g for 10 min. The supernatant (150 ml) was added to each well of a Biolog GN plate. Absorbance of the wells at 590 nm was read using a VMAX (Molecular Devices, Crawley, UK) automated plate reader. The plates were then sealed inside a plastic bag and incubated at 25 8C in the dark, and read every 24 h over 7 d. The zero hour data were subtracted from each of the subsequent measurement times in order to remove the background absorbance. The average absorbance (average well colour development, AWCD) was then calculated for each plate and plotted against time to give AWCD curves. The incubation time data with AWCD closest to 1.00 were chosen for final data analysis, and normalized by dividing the absorbance of each well on a plate by the AWCD of that plate. The values for well number 1 (contains no C source) were removed and the data analysed by principal components analysis (PCA) using the covariance matrix. 2.6. PLFA analysis Soil samples (approximately 10 g wet weight) were frozen and stored at 2 20 8C. All samples were then
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analysed simultaneously at the end of the experiment. PLFAs were extracted and analysed as described by Tunlid and White (1992), using a modified Bligh and Dyer extraction procedure (Bligh and Dyer, 1959). Briefly, the soil lipids were extracted in a one-phase chloroform/ methanol/citrate buffer mixture, and the phospholipids separated by liquid chromatography over silica. The phospholipids were then derivatized to their corresponding methyl esters, and analysed by GC –FID as described by Frostega˚rd et al. (1996). PLFA identities were assigned based on retention time and comparison with known standards and were verified by GCMS. Saturated and unsaturated FAMES were identified by making silver adducts and the position of unsaturated bonds was determined using disulphide bridging. The position of the double bonds in one PLFA identified as an 18:2 were either 18:2 v8,12 or 18:2v8, 13 and is reported as 18:2v8, 12(13). 2.7. Total petroleum hydrocarbon determination Approximately, 10 g soil (wet weight) was ground over copious anhydrous dichloromethane (DCM)-washed Na2SO4 using a mortar and pestle. The sample was transferred to a 250 ml conical flask equipped with a PTFE-lined screw cap, and an internal standard (pristane) added. Samples were extracted by sonication in 50 ml of DCM for 10 min and the extract was then filtered (Whatman no. 4). The extraction was repeated with 25 ml of DCM, filtered, and the two extracts combined. Five milliliters of extract was stored at 2 20 8C in a foil-capped scintillation vial; the remainder was evaporated under N2 at 40 8C to a volume of 1 ml. The extract was then cleaned by liquid ˚ , , 200 mm) that chromatography over 2 g of silica (60 A had been conditioned with 10 ml of DCM. The sample was loaded onto the column and eluted with 2 £ 10 ml of DCM. TPH was then measured by GC – FID. One microliter of extract was injected using an autosampler onto a Carlo-Erba 8000 series GC (ThermoQuest Ltd, Wythenshawe, UK) equipped with a Phenomenex ZB-1 capillary column (30 m £ 0.32 mm, d ¼ 0:5 mm), a split ratio of 20:1, and flame ionization detector. GC conditions were as follows: column gas flow 1 ml (N2) min21; injector temperature, 250 8C and detector temperature, 320 8C. The temperature programme used was 80 8C, held for 2 min, then increased at 10 8C min21 to 320 8C and held for 10 min. TPH concentration was calculated as area relative to the internal standard peak area. 2.8. Data analysis All multivariate analyses were carried out in Simca-P 8.0 (Umetrics, Umea˚, Sweden). PCA was carried out for both PLFA and Biolog profiles on mean-centred normalized data (i.e. data expressed as a percentage of the average value of the set) using the covariance matrix. The normalized PLFA data were log-transformed prior to PCA, but not otherwise
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scaled. This has the effect of downweighting the relative importance of the PLFAs present in high concentrations, which would otherwise dominate the PCA. However, because the data are not transformed to unit variance (in other words, not analysed using the correlation matrix), the ‘information’ as to which PLFAs are more biochemically and ecologically important, i.e. which are found in higher quantities, is still preserved within the dataset. This is also an unbiased way of downweighting the relative importance of PLFAs present in very low concentrations—which tend to have higher variances because of analytical uncertainty, and can thus unrealistically affect PCA when data are scaled to unit variance. Further details of manipulation of data from sole C source utilization tests prior to PCA are given above. The CLPP and PLFA data were further analysed at individual sampling times using partial least squares discriminant analysis (PLSDA), in order to distinguish treatment-related changes from changes caused by incubation of the soil. The reader is referred to the relevant literature for a full description of partial least squares analysis (Eriksson et al., 1995). Briefly, PLSDA is a ‘supervised’ technique, unlike PCA, meaning that the samples are assigned to classes based on prior knowledge (in this case, experimental treatment). Axes are fitted to a dataset as for PCA, but in partial least squares analysis the fitting algorithm is calculated both to explain a high proportion of the variance within the data, and also to maximize correlation with an external variable or variables. Assigning a class variable, or factor, to samples according to experimental treatment results in PLSDA. Adding more axes to a PLSDA model will increase the apparent amount of data explained, but too many axes will overfit the model and give spurious results. Care should be taken to avoid overfitting. Cross-validation is a simple and powerful technique for doing so: the data are divided into a number of groups at random, and each group in turn is omitted from the data and the analyses re-run. The predicted value for each omitted data point can then be subtracted from the actual value to generate a set of ‘predictive residuals’. These can be used to produce the q2 summary statistic, analogous to the r 2 statistic (Eriksson et al., 1997):
then cleared by centrifugation at 3000g for 30 min. The strains used were V. fischeri (the Microtoxw acute assay, commonly referred to as the ‘luminescent bacteria test’) and the hydrocarbon degrader P. putida F1 (pUCD607) (H.J. Weitz, Unpublished Thesis, University of Aberdeen, 2000). V. fischeri is naturally bioluminescent, whereas the P. putida strain has been marked with the multi-copy plasmid pUCD607, which contains the constitutively-expressed luxCDABE cassette. Freeze-dried cultures were used for both strains. All bioassays were carried out in triplicate, and light output measured using a Jade portable luminometer (Labtech, Uckfield, UK). Full experimental details are reported in Bundy et al. (2001); a brief summary is given here. The luminescence was compared to that of control soil extracts and expressed as percent inhibition. V. fischeri cell suspensions (10 ml) were added to 100 ml methanol extract, 100 ml 22% (w/v) NaCl solution, and 790 ml deionized water. Luminescence was read after 15 min. P. putida F1 (pUCD607) cells were rehydrated in 10 ml 100 mM KCl for 60 min, then 100 ml cell suspension added to 100 ml methanol extract and 800 ml 113 mM KCl, i.e. such that the final KCl concentration in the cuvette was 100 mM. Luminescence was read after 30 min.
3. Results 3.1. Removal of oils The loss of oils as demonstrated by decline in TPH showed (Fig. 1) that paraffin was removed more rapidly and to a greater extent than motor oil, with less than 10% remaining by the end of the experiment. More than 50% of motor oil was removed by day 103. The bulk soil microbial variables measured, basal respiration and microbial biomass, indicated that the highest measured biodegradative activity was at day 27 when both respiration and biomass were significantly elevated over control soils ðP , 0:05Þ for both oil types (Fig. 2). The soil pH was unaffected in both motor oil- and paraffin-treated soils ðP . 0:05Þ; but in
q2 or r 2 ¼1 2 ðresidual sum of squaresÞ=ðtotal sum of squaresÞ If standard residuals are used, the r 2 value is obtained. If the predictive residuals are used, the q2 value is obtained. A high q2 value indicates a model with good predictive power that is not overfitted by inclusion of too many axes. 2.9. Bioluminescent bioassays Methanolic extracts were made for biosensor testing. Soil (2 g wet weight) was extracted by sonication for 15 min at room temperature in 4 ml of methanol. The extracts were
Fig. 1. Change in total petroleum hydrocarbon concentration over time. Data are mean ^ SE ðn ¼ 3Þ: Curves represent fitted exponential regressions. A: paraffin. B: motor oil.
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Fig. 2. Response of soil microbial community bulk variables to oil contamination and remediation over time, compared to control soils. A: basal respiration. B: biomass, expressed as total PLFAs. Open circles: controls. Filled squares: paraffin. Filled triangles: motor oil. Error bars represent ^ SE ðn ¼ 3Þ:
the control soils dropped significantly from pH 5.9 at the beginning of the experiment to pH 5.5 by day 62 and pH 5.1 by day 103 ðP , 0:001Þ: 3.2. Toxicity to bacteria The two bioluminescent bioassay organisms employed gave quite different responses to the two oils and the progress of bioremediation. P. putida F1 (pUCD607) was more sensitive to paraffin than to motor oil, with the luminescence of the treated soil extracts below 50% of the control soils at all times (Fig. 3). The maximum toxicity was observed at day 27 for both oils. V. fischeri (Microtox; the luminescent bacteria test) had an opposite response, with paraffin being less toxic, actually stimulating light output at day 1. Luminescence inhibition increased until day 62, and had been partially reversed by the end of the experiment. Motor oil was much more toxic at all times, but also showed maximum inhibition of luminescence at day 62. 3.3. Effects on Biolog CLPPs PCA is an unsupervised technique that is an aid to graphical visualization in changes in the data. It is an appropriate method for looking at natural patterns in data.
Fig. 3. Toxicity during course of bioremediation shown by response of luminescent bioassays to contaminated soil extracts. Error bars represent ^ SE ðn ¼ 3Þ: A: paraffin contaminated soils, P. putida F1 (pUCD607) response. B: paraffin contaminated soils, V. fischeri response. C: motor oil contaminated soils, P. putida F1 (pUCD607) response. D: motor oil contaminated soils, V. fischeri response.
A scores plot of PC 1 against PC 2 for the CLPP data (Fig. 4) shows that the main source of variation within the data is the effect of incubation time: the early samples (from days 1 and 27) have low PC 1 scores, whereas the later samples (from days 62 and 103) have high PC 1 scores. There was a statistically significant ðP , 0:0001Þ correlation of 0.65 between sample time and PC 1 scores. The control soil profiles shifted within principal component space as a function of incubation time as least as much as the contaminated samples. There were no consistent or interpretable changes that could be ascribed to the C sources which were responsible for the differences observed (data not shown). PLSDA is a supervised method, and thus presents systematic differences between sample classes. For all of the CLPP and PLFA models described here, the summary statistics (r 2 ; q2 ; and number of significant components fitted) are given in Table 1. For the day 1 data no significant components could be fitted, indicating that the treatments
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Fig. 4. PCA scores plot of CLPP data, all time points analysed simultaneously. Circles: controls; Squares: paraffin; Triangles: motor oil. Size of symbol represents sampling date: smallest symbols ¼ day 1, largest symbols ¼ day 103.
had not affected the physiological profiles. For the day 27 data, only one PLS component was significant (Fig. 5), separating the control from the treated samples. For the day 62 data, two significant PLS axes can be fitted. The controls are clearly separated along PLS axis 1, whereas the two oil types are separated along PLS axis 2. By day 103, the end of the experiment, once more no significant components could be fitted.
the dataset. However, there was also a shift in control soil community profiles along PC 2 as an effect of incubation time. The control soil profiles had positive PC 1 scores at all sampling dates. The oil-contaminated soils had positive PC 1 scores on day 1, which were not significantly different from those of the control soils on day 1. By day 27, however, all the oil-contaminated soils had negative PC 1 scores, which remained negative and significantly different from the controls for the rest of the experiment. PLSDA was again used to visualize specific differences between groups of samples. As for the CLPP data, no model could be fitted to the day 1 data. Again, as for the CLPP data, the day 27 data were fitted by a model with one significant component, with controls separated from oiled soils along axis 1. For both the day 62 and the day 103 data, a two-component model could be fitted, which separated control from oiled soils along axis 1, and separated the two oil types along axis 2 (Fig. 7). Because of the apparent similarity of the days 62 and 103 data, the data from these two time-points were reanalysed in a single PLSDA model. A scores plot and a loadings plot for this model are both given in Fig. 8. The scores plot confirms that the control soils are separated from oiled soils along axis 1, whereas the two oil types are separated along axis 2. Inspection of the loadings along axis 1 (Fig. 8) shows that the control soils are associated with PLFAs 12:0, 18:0 (10Me), 17:0ai, 17:0br, and 19:0cy, and the oiled soils are associated with 18:1v9, 15:v0, 17:16, and 18:2v6,9. Inspection of the loadings along axis 2 shows that the paraffin-contaminated soils are associated with 15:0 and 17:1v8, and the motor oil-contaminated soils are associated with 18:2v6,9 and 15:0ai.
3.4. Effects on PLFA profiles All the samples contained a variety of PLFAs composed of saturated, unsaturated, methyl-branched, and cyclopropane fatty acids. In all 37 PLFAs with chain length from C12 to C20 were identified and used in the PCA. There was a clear effect of oil treatment along PC 1 (Fig. 6), i.e. the applied treatment was the major source of variation within
4. Discussion 4.1. Disappearance of oils The disappearance of the different oils as seen in this experiment (Fig. 1) may be by a combination of losses
Table 1 q2 and r 2 values calculated from partial least squares analysis for PLFA and CLPP experimental data Samplea
No. of components
CLPP 1 CLPP 27 CLPP 62 CLPP 103 PLFA 1 PLFA 27 PLFA 62 PLFA 103 PLFA 62/103
0 1 2 0 0 1 2 2 2
q2 First
q2 Second
q2 Cumulative
r 2 First
r 2 Second
r 2 Cumulative
0.36 0.27
0.48
0.62
0.46 0.49
0.35
0.85
0.31 0.42 0.32 0.43
0.73 0.43 0.48
0.84 0.61 0.7
0.5 0.49 0.44 0.49
0.47 0.33 0.35
0.96 0.77 0.84
q2 and r2 values represent summary statistics for predictive power and goodness of fit, respectively (Eriksson et al., 1997). A positive q2 value indicates that the PLS model has not been overfitted. a Number following the sample code represents day of sampling.
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Fig. 5. PLSDA scores plot of CLPP data, analysed for each time point separately. Circles: controls; Squares: paraffin; Triangles: motor oil. A: day 27 data; note that only axis 1 is significant, and axis 2 is included only to make the data easier to visualize. B: day 62 data.
through abiotic processes, primarily volatilization, and through biodegradation. No attempt was made to judge the fraction lost through abiotic processes, for example by internal biomarkers (Bragg et al., 1994; Wang et al., 1998), which would be necessary to judge bioremediation efficiency. Our study, however, was intended to test the relative response of microbial variables to a realistic bioremediation model, including both degradation and abiotic losses. The increase in microbial biomass and in basal respiration (Fig. 2) with the concomitant decrease in TPH is evidence that an active hydrocarbon-degrading community had developed during the experiment. As might have been expected due to its higher proportion of lighter fraction hydrocarbons, the paraffin was degraded more quickly and to a greater extent than the motor oil.
Fig. 6. PCA scores plot of PLFA data, all time points analysed simultaneously. Circles: controls; Squares: paraffin; Triangles: motor oil. Size of symbol represents sampling date: smallest symbols ¼ day 1, largest symbols ¼ day 103.
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Fig. 7. PLSDA scores plot of PLFA data, analysed for each time point separately. Circles: controls; Squares: paraffin; Triangles: motor oil. A: day 27 data; note that only axis 1 is significant, and axis 2 is included only to make the plot easier to visualize. B: day 62 data. C: day 103 data.
4.2. Difference between oil types In a previous study we compared Biolog CLPPs with PLFA profiles for the assessment of the effects of diesel in three different soil types (Bundy et al., 2002). Diesel treatment elicited large multivariate changes for both techniques, and demonstrated that, counter-intuitively, the CLPP and PLFA profiles of microbial communities in
Fig. 8. PLSDA of PLFA days 63 and 102 data. A: scores plot. Circles: controls; Squares: paraffin; Triangles: motor oil. B: loadings plot. Individual PLFAs which are influential in the PLSDA separation (defined in this case as [(axis 1 loading)2 þ (axis 2 loading)2]1/2 . 0.2) are labelled directly on the plot.
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the different soil types became less similar following diesel contamination. We have also used lux-marked bacterial bioassays to assess the toxicity of soils contaminated with crude oils (Bundy et al., 2001). In this study, we tested if combining multivariate profiling techniques (PLFA analysis, CLPPs) with bacterial bioassays for general toxicity gave complementary information that could potentially be useful for following the course of remediation at a contaminated site. 4.2.1. Toxicity Previous microbial toxicity studies have shown an initial increase in toxicity as an initial phase of bioremediation, followed by a recovery to non-toxic levels (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). The results of our study are not so clear. Out of the four possible oil/bioassay combinations, three showed some recovery, i.e. decrease in toxicity, by the last sampling time (Fig. 3). Only the response of V. fischeri to motor oil showed no recovery at all, with this combination giving the highest degree of inhibition (. 90% at days 62 and 103). Clearly, our simulated treatment neither completely removed TPHs from the soil, as judged by chemical analysis, nor entirely ameliorated the toxicity, as judged by the bioassays. A more vigorous bioremediation treatment (e.g. including additional aeration or mixing of soil) might have led to a decrease in toxicity, as recorded in previous studies (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). The original hydrocarbons in the refined oils are likely to act purely through a non-specific narcosis mechanism of action, by partitioning into cell membranes (Van Wezel and Opperhuizen, 1995). The degree of toxicity is related to their hydrophobicity ðlog Kow Þ; although very hydrophobic compounds may not be soluble enough to elicit a toxic response under the bioassay conditions used (Bundy et al., 2003). Furthermore in soil, very hydrophobic compounds are likely to be sequestered into soil organic matter or into oil droplets (Tang et al., 1998), and hence may not exert a toxic effect. Consequently, it has been observed for hydrocarbons that the initial degradation products, which usually are carboxylic acids (Leahy and Colwell, 1990), are more toxic than the parent hydrocarbons (Long and Aelion, 1999). This may well be entirely due to their increased solubility and hence availability. A second possible mechanism is that the indigenous microbes reacted to the presence of these oils by producing biosurfactants, and that the presence of the biosurfactants increased the toxicity of the hydrocarbons by rendering them more available (Mihelcic et al., 1993). This second alternative seems less likely than the accumulation of initial degradation products, simply because the methanol extraction step would tend to reduce the influence of biosurfactants on extractability, certainly when compared to an aqueous extraction. It was unexpected that the two microbial bioassays gave apparently opposite results for judging the toxicity of the two refined oils—paraffin was more toxic than motor oil to
P. putida F1 (pUCD607) at all sampling times, whereas the converse was true for V. fischeri. The precise reason for this is unknown, but may be related to the constraints of the experimental system. For example, we have observed (for a similar lux-marked bioassay strain) that there is a threshold of hydrophobicity above which compounds appear to be non-toxic, even in the presence of a co-solvent (Bundy et al., 2003). Motor oil contains much heavier, more hydrophobic compounds than paraffin, and might thus exert less of an effect on P. putida F1 (pUCD607). In comparison, V. fischeri is sensitive to compounds of high octanol:water partition coefficient (log Kow values . 5; Hermens et al., 1985). However, this does not explain why P. putida F1 (pUCD607) is more sensitive to paraffin than V. fischeri, and is evidence that relying only on a single-species bioassay test is not likely to give reliable results. It is evident that microbial luminescent bioassays alone do not provide an unequivocal means of following the progress of disappearance of the refined oils. We hypothesized that a combination of different community-level techniques with the toxicity assays would provide a more complete picture. The PLFA analysis and CLPP together confirm that the microbial communities were affected as a result of the oil treatment, with treated communities completely separated within principal component space by day 27. As for the toxicity bioassays, the multivariate techniques did not provide clear evidence that there was any ecological recovery of the communities by the end of the experiment: the CLPPs did not distinguish controls from oiled soils, i.e. indicating that there was recovery, but the PLFAs showed that there were still clear differences. 4.2.2. Development of different communities The luminescent bioassays demonstrated that there were some apparent differences between the microbial responses to the two refined oil types. The community-level profiling techniques are much more powerful in being able to discriminate these effects, and are also a direct measure of the indigenous communities, or at least a subset of the culturable indigenous bacteria in the case of the CLPP. Multivariate data analysis allows one to extract information that would not be obvious or easy to obtain by other methods. For example, it is clear that the initial response to both paraffin and motor oil was a generalized response, i.e. was not specific to oil type, even though the two oils have very different physical properties and hydrocarbon compositions. For both of the multivariate techniques, CLPP and PLFA profiling, PLSDA gave only one significant component by day 27 (Figs. 5 and 7). This is a strong indication that the initial community response to oil contamination was nonspecific, probably both through selection for very similar generalist microbes (Atlas et al., 1991) and through general toxic effects of the oils on the community. However by day 62, the paraffin- and motor oil-contaminated soil communities had diverged, as shown by both PLFA and CLPP.
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These differences between the oils were possibly caused by gradual development of more specialist communities, which were able to degrade the different hydrocarbons found in the different oil types. These oil-induced community differences clearly persisted for the duration of the experiment as shown by the PLFA data, which separate the samples at days 62 and 103 by oil type along axis 2 (Fig. 8). It is interesting that the individual fatty acids associated with the paraffin-affected community are 15:0 and 17:1v8, while the fatty acid most highly associated with the motor oil affected community is 18:2v6,9, generally considered to be a marker for fungi in soil (Pennanen et al., 1998). Possibly, the fungi were more able to degrade the aromatic hydrocarbons that would be found in motor oil. It has also been found that linear hydrocarbon (alkane) contamination may directly alter microbial lipid profiles, by becoming incorporated into the PLFAs (Doumenq et al., 1999, 2001). This might perhaps explain the appearance of 15:0 as a fatty acid marker discriminating paraffin from motor oil—the motor oil that we used did not contain any n-alkanes as short as 15 C atoms, as shown by the GC – FID traces, whereas paraffin would likely have contained such short- to medium-length n-alkanes. In contrast, the CLPPs are separated according to oil type within the first two PCs for the day 62 data only, and by day 103 there were no significant effects at all. It is likely that the C substrates in the Biolog GN plates are only able to distinguish broadly between normal and fast-growing, hydrocarbon-degrader-enriched communities. It is still possible that if more suitable C substrates relevant to hydrocarbon degradation could be chosen, or if the assay conditions were changed (Campbell et al., 1997), or whole soil used instead of soil extracts (Campbell et al., 2003) then this might improve discrimination between different oil types by CLPPs. However, an earlier attempt to use carboxylic acids as C sources ecologically relevant to refined oil degradation failed to show any improvements over the standard Biolog GN carbon sources (Bundy et al., 2002). 4.3. Incubation effects in control One of the major influences on the microbial communitylevel responses was the effect of incubation time on the microbial PLFA and CLPP profiles in the control soil. For the CLPPs in particular, this was the single biggest effect on the community profiles, with samples separated by incubation time along PC 1 (Fig. 4). The effect was also noticeable in the PLFA profiles, although in this case, oil treatment gave the largest effect. This incubation effect made it difficult to properly assess the progress of oil disappearance using CLPPs alone, and the effects of oils on the PLFA profiles were certainly clearer. The precise reason for the changes observed in the CLPPs and PLFA profiles with time are not known, but the microbial community of soils is known to be affected by sampling and storage
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(Petersen and Klug, 1994). In our investigation, there was a decrease in soil pH in the control but not in the oiled soils, which clearly indicates that there were time-related changes. These pH-related effects parallel the changes observed in the PLFA profiles, where the main change is caused by oil treatment (separation along PC 1), but there is also a clear separation of the control soils at the beginning and at the end of the experiment, orthogonal to the effects of the oil (Fig. 6). 4.4. Advantages/disadvantages of selected techniques The techniques we used have been applied separately to assess the effects on soil microbes of oil contamination and bioremediation. PLFA community profiling has probably been the most widely used technique, and only a couple of instances will be mentioned here. Stephen et al. (1999) compared PLFA profiles with PCR-DGGE for samples taken from a JP-4 fuel contaminated site. They found that PLFAs characteristic of sulphate-reducing bacteria were more common around the fringes of the spill, and that trans/ cis ratios were higher within the spill, indicating higher amounts of physiological stress. Macnaughton et al. (1999) also used PLFA profiling, as well as 16S rDNA community profiling using DGGE, to follow the effects of crude oil contamination on beach sands. Some recovery of PLFA profiles was observed, but the changes were dominated by a time effect, ascribed to eukaryotic PLFAs derived from invertebrate eggs found in the sands. This and our own study suggest PLFA community analysis can therefore be used as a profiling technique to follow the effects of oil contamination on soil microbes, and has the great advantage of not relying on growth. However, it may not be ideal as a sole technique, as many of the PLFAs are common to many different microbial taxonomic groups, and it is therefore difficult to relate changes to specific members of the microbial community (White et al., 1998). CLPPs based on Biolog microplates have also been used for assessing oil pollution. Wu¨nsche et al. (1995) compared remediation of a contaminated refinery soil with a spiked pristine arable soil. The largest effect they observed was that the spiked soil CLPPs changed much more than did those of the refinery soil. Lindstrom et al. (1999) examined the site of an aged oil spill at Arctic latitudes. They took the novel and interesting approach of comparing the multivariate response of inocula of different dilutions of the extracted soil bacteria. The number of substrates utilized by the control site samples was reduced more at high dilutions than for the contaminated samples, implying that there had been an increase in metabolically versatile, generalist bacteria at the contaminated site. This might be expected as it has been suggested that oil contamination reduces microbial biodiversity, but increases catabolic diversity by selecting for competitive, generalist type strains (Atlas et al., 1991). The CLPP approach has been strongly criticised due to its dependence on culturing (Konopka et al., 1998).
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Nonetheless, it is still likely that useful information on the subset of culturable bacteria can be obtained using this method (Garland, 1997) especially if hydrocarbons do select for fast-growing bacteria (Kozdro´j and van Elsas, 2000). The naturally luminescent bacterium V. fischeri (the Microtox acute assay) has been used several times for following the course of bioremediation, and was included here to permit comparison with previous work. However, it is a marine organism, and highly sensitive to physical factors such as osmotic strength, and so its response might not be appropriate for monitoring terrestrial pollution. For example, apparent toxicity to V. fischeri was shown to increase considerably in the presence of soil extract, whereas the same effect was not seen with a soil organism (Bundy et al., 1997). P. putida F1 (pUCD607) is a hydrocarbon-degrading soil organism, and therefore highly ecologically relevant to studies of oil contamination and bioremediation.
5. Conclusion The different oil types, the light paraffin and heavy motor oil, increased the toxicity to soil microbes compared to control soils, but there were indications that this toxicity was ameliorated as bioremediation progressed. The use of community level techniques provided totally different information—thus it was evident that the initial response of the soil community to oil contamination was general, with no distinction between motor oil and paraffin, but that with the passage of time more specialist communities developed. Thus the use of community profiles and singlespecies toxicity bioassays do provide complementary information to assess the mechanisms and events associated with the recovery of soils contaminated by oil hydrocarbons. Oil-induced changes were more clearly shown by the PLFA profiles, as the CLPPs were very sensitive to changes caused just by incubation of the soils.
Acknowledgements J.G. Bundy was funded by the award of an Aberdeen Research Consortium PhD studentship. C.D. Campbell was funded by the Scottish Executive, Environment and Rural Affairs Department.
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Combined microbial community level and single species biosensor responses to monitor recovery of oil polluted soil Jacob G. Bundya,b,1, Graeme I. Patonb, Colin D. Campbella,* b
a The Macaulay Institute, Craigiebuckler, Aberdeen AB15 8QH, UK Department of Plant and Soil Science, University of Aberdeen, Cruickshank Building, St Machar Drive, Aberdeen AB24 3UU, UK
Received 24 September 2002; received in revised form 11 February 2004; accepted 24 February 2004
Abstract Understanding the effects of oil contamination on the composition and function of soil microbiota entails investigation of the effects of a mixture of hydrocarbons at the community level in a complex environmental matrix. One approach to this difficult problem is to ally a community-level fingerprinting approach with bioassays that have a physiological or functional implication. Two contrasting refined oils (paraffin and motor oil) were used to contaminate soil microcosms, and a simulated bioremediation treatment with nutrient-addition was applied. The indigenous microorganisms were monitored over 103 d using complementary community-level techniques (carbon source physiological profiling using Biologw microplates, and phospholipid fatty acid (PLFA) profiling). Changes in the toxicity of the applied oils were monitored using luminescent bacterial bioassays, including Vibrio fischeri and a hydrocarbon-degrading Pseudomonas putida strain. Distinct shifts in microbial community structure and C source utilization profiles were observed as a result of oil contamination. There was some evidence that bioremediated soils were returning to control values by the end of the experiment. This was supported by the bioassay results which showed an initial increase in toxicity as a result of the oil addition which had then decreased by the conclusion of the experiment. The two oils exhibited markedly different toxicity towards the bioassay organisms, with species-specific differences in response. This oil-specific difference was also found in the PLFA profiles which showed the two oil types selected different microbial communities. q 2004 Elsevier Ltd. All rights reserved. Keywords: Oil contamination; Bioremediation; Carbon substrate utilization; Phospholipid fatty acid analysis; Community structure
1. Introduction Soil contamination by petroleum products is a widespread problem, with many hotspots of pollution arising from individual spills (Whittaker et al., 1995). Cleanup of these contaminated sites is an important goal, and bioremediation is a low-input and cheap approach to remove hydrocarbons. Many related approaches can be described as bioremediation, including active techniques such as addition of nutrients or surfactants, or alternatively passive techniques such as natural attenuation, for each of which it is critically important to monitor the progress of oil disappearance. The current study focuses on nutrientassisted bioremediation, which relies on the natural soil * Corresponding author. Tel.: þ44-1224-498-200; fax: þ 44-1224-498207. E-mail address: [email protected] (C.D. Campbell). 1 Present address: Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 0038-0717/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2004.02.025
community but adds exogenous sources of N and P to increase the rate of hydrocarbon degradation. If bioremediation is the strategy of choice, accurate and reliable methods for following the course of biodegradation are essential. Simple residue concentration-based targets, such as a maximum allowable amount of total petroleum hydrocarbons (TPH), are common, but have been criticized on the grounds that this conveys nothing about the differing chemical composition of different oils, which will have different toxicities, degradability, and breakdown products (Michelsen and Boyce, 1993; McMillen et al., 1995). It has been suggested as an alternative that the demonstration of mitigation of biological effects, or reduction of risk, could form the basis of a more biologically relevant endpoint (Salanitro et al., 1997; White et al., 1998; Stroo et al., 2000). Earthworm and other invertebrate, microbial and plant bioassays (Salanitro et al., 1997; Saterbak et al., 1999; van Gestel et al., 2001) have all been studied. However, given the importance of understanding soil microbial ecology to
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bioremediation (Holden and Firestone, 1997), it seems logical that the effects on the indigenous microbiota should also be determined. As the soil microbial community plays a major role in soil functioning, in particular C turnover and other nutrient transformations, it is therefore important to understand any potential negative effects of oil pollution. Two broad approaches can be defined for investigating contaminant effects on microbes. Firstly, effects on the entire community can be measured using a community-level multivariate (profiling) technique. In practice, no single technique currently in use reports on the entire soil microbial community (Madsen, 1998; Ogram, 2000; Kozdro´j and van Elsas, 2001). Culture-based methods measure only a small fraction of the community; soil DNA profiling methods, such as PCR followed by denaturing gradient gel electrophoresis (DGGE), may be restricted by selection of primers, and PCR bias is also likely to affect the results obtained. Biases caused by non-quantitative extraction of nucleic acids from soil are also likely (Martin-Laurent et al., 2001). Similarly, nonDNA based molecular methods such as phospholipid fatty acid (PLFA) profiling do not report on the Archaea (Zelles, 1999). It is therefore sensible to use a combination of techniques. It has been suggested that microbial community response could form the basis of a measure of remediation success by demonstrating that the community response has followed a trajectory through multivariate dimensions, from control to adversely affected, and then returns to control profiles (White et al., 1998). The second approach is to look more closely at the properties or mechanistic effects of a stress event (such as oil contamination) by employing single-species microbial tests and bioassays. Studies using the luminescent bacteria test (Vibrio fischeri, Microtox) have shown that bioremediation causes an initial increase in toxicity followed by a return to control levels (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). However, there are drawbacks to using a marine organism such as V. fischeri for terrestrial ecotoxicity assessment (Paton et al., 1997). Consequently, genetically modified (lux-marked) bioluminescent bacteria, using more ecologically-relevant species, have been used in toxicity bioassays to assess oil bioremediation (Bundy et al., 2001). There are likely to be advantages to combining the community-level approach with complementary singlespecies toxicity bioassays to monitor the effects of refined oil contamination and bioremediation on soil microorganisms because the community response gives an indication of medium- and long-term effects, whereas the bioassays can report on the potential acute toxic response to pollutants (Brohon et al., 2001). Hence, comparison of community profiling techniques with toxicity response data obtained from single organisms may lead to a better understanding of the toxic effects of oil contamination. Our aim was to compare community-level fingerprinting techniques (PLFA profiling and C substrate utilization profiling) with single-species microbial toxicity bioassays,
in an attempt to provide complementary information on the response of soil microcosms contaminated with light-refined paraffin or motor oil, and how the response changes with time. The specific hypotheses we addressed were: (a) that different oil types, light and heavy, would select different soil microbial communities; and (b) that ecological recovery after oil contamination will be manifested at the community level by a return to original constitution of microbial profiles, and at the physiological level by a reduction in the toxicity of the oil and its initial degradation products towards the singlespecies bioassays. The response of the marine bacterium V. fischeri, as a standard bioassay organism, was compared to a more environmentally-relevant hydrocarbon-degrading soil organism, Pseudomonas putida.
2. Materials and methods 2.1. Oil and soil properties Soil (cambisol; Insch series, Insch association) was taken from a grassland site with no history of petroleum contamination. Soil was collected from the A horizon, sieved (5 mm) field-moist through a stainless-steel sieve, and stored at 4 8C until use. The soil was stored for less than 1 month. The soil had a sandy loam texture, an organic C content of 2.1%, and a pH (CaCl2, 10 mM) of 5.9. Standard commercial-grade motor oil and paraffin were purchased from a local supplier and used without modification. 2.2. Experimental design Approximately 60 g wet weight of soil was placed without packing into a 250 ml glass jar and the jar sealed with stretched parafilm. The jars were kept at 25 8C in the dark at 90% relative humidity. Triplicate sets of jars were contaminated with either paraffin, motor oil or left as controls. All microcosms including the controls were treated with nutrient solution (see below). Triplicate microcosms were destructively sampled at each sampling time. There were four sampling dates, hence 36 microcosms in total were used. Microcosms were arranged in a random order, and rearranged every 2 –3 weeks throughout the experiment to compensate for any local temperature fluctuations in the incubator. The microcosms were sampled at 1, 27, 62, and 103 days. Upon sampling, the soil within each jar was mixed and portions of soil were then used for Biolog communitylevel physiological profiling (CLPP, 10 g soil), PLFA analysis (10 g), respiration and water content (20 g), pH (5 g), TPH (10 g), and bioassay analysis (2 g). 2.3. Oil and nutrient addition Moist soil, at approximately 40% of water holding capacity (WHC), was spiked by manual mixing with the paraffin or motor oil. The soil was spread thinly and evenly
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in a large glass dish; the oil was poured evenly over the surface, and then mixed with a stainless steel spatula for 5 min. Oil was added at the rate of 20 g kg21 dry weight of soil (2%). This concentration is typical of bioremediation studies and is well within the reported range of environmental contamination by petroleum products (Whittaker et al., 1995). Nutrient solution was then poured evenly over the soil in order to simulate a bioremediation treatment. Additional water was added to bring the final moisture content of each soil to 55% of WHC. The soil was then mixed for a further 5 min. The nutrient solution contained NH4NO3 as the N source, and P as a K2HPO4/KH2PO4 buffer solution with a pH of 6.4. The final solution contained 28.0 g N l21 and 3.10 g P l21, resulting in a final oil:N:P ratio of 100:10:1 (approximate C:N:P ratio 100:12:1.25). 2.4. Respiration Basal respiration was measured by monitoring CO2 evolution using a GC (Perkin Elmer F33, Cambridge, UK) equipped with a packed column and thermal conductivity detector. Concentrations were calculated relative to a CO2 standard mix of 0.13% (v/v). Soil was taken from the jar upon sampling and placed into a fresh sealed container, and the container then incubated at 25 8C for a further 6 h. CO2 concentrations were then sampled every 2 h for this 6 h period, and the rate of CO2 increase calculated. 2.5. Biolog community-level physiological profiling Soil (10 g wet weight) was added to 100 ml of deionized water in 250 ml conical flasks and shaken for 10 min on a wrist-action shaker. The resulting aqueous extract was used for the basis of a 10-fold dilution series. The 1024 dilution was then centrifuged at 100g for 10 min. The supernatant (150 ml) was added to each well of a Biolog GN plate. Absorbance of the wells at 590 nm was read using a VMAX (Molecular Devices, Crawley, UK) automated plate reader. The plates were then sealed inside a plastic bag and incubated at 25 8C in the dark, and read every 24 h over 7 d. The zero hour data were subtracted from each of the subsequent measurement times in order to remove the background absorbance. The average absorbance (average well colour development, AWCD) was then calculated for each plate and plotted against time to give AWCD curves. The incubation time data with AWCD closest to 1.00 were chosen for final data analysis, and normalized by dividing the absorbance of each well on a plate by the AWCD of that plate. The values for well number 1 (contains no C source) were removed and the data analysed by principal components analysis (PCA) using the covariance matrix. 2.6. PLFA analysis Soil samples (approximately 10 g wet weight) were frozen and stored at 2 20 8C. All samples were then
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analysed simultaneously at the end of the experiment. PLFAs were extracted and analysed as described by Tunlid and White (1992), using a modified Bligh and Dyer extraction procedure (Bligh and Dyer, 1959). Briefly, the soil lipids were extracted in a one-phase chloroform/ methanol/citrate buffer mixture, and the phospholipids separated by liquid chromatography over silica. The phospholipids were then derivatized to their corresponding methyl esters, and analysed by GC –FID as described by Frostega˚rd et al. (1996). PLFA identities were assigned based on retention time and comparison with known standards and were verified by GCMS. Saturated and unsaturated FAMES were identified by making silver adducts and the position of unsaturated bonds was determined using disulphide bridging. The position of the double bonds in one PLFA identified as an 18:2 were either 18:2 v8,12 or 18:2v8, 13 and is reported as 18:2v8, 12(13). 2.7. Total petroleum hydrocarbon determination Approximately, 10 g soil (wet weight) was ground over copious anhydrous dichloromethane (DCM)-washed Na2SO4 using a mortar and pestle. The sample was transferred to a 250 ml conical flask equipped with a PTFE-lined screw cap, and an internal standard (pristane) added. Samples were extracted by sonication in 50 ml of DCM for 10 min and the extract was then filtered (Whatman no. 4). The extraction was repeated with 25 ml of DCM, filtered, and the two extracts combined. Five milliliters of extract was stored at 2 20 8C in a foil-capped scintillation vial; the remainder was evaporated under N2 at 40 8C to a volume of 1 ml. The extract was then cleaned by liquid ˚ , , 200 mm) that chromatography over 2 g of silica (60 A had been conditioned with 10 ml of DCM. The sample was loaded onto the column and eluted with 2 £ 10 ml of DCM. TPH was then measured by GC – FID. One microliter of extract was injected using an autosampler onto a Carlo-Erba 8000 series GC (ThermoQuest Ltd, Wythenshawe, UK) equipped with a Phenomenex ZB-1 capillary column (30 m £ 0.32 mm, d ¼ 0:5 mm), a split ratio of 20:1, and flame ionization detector. GC conditions were as follows: column gas flow 1 ml (N2) min21; injector temperature, 250 8C and detector temperature, 320 8C. The temperature programme used was 80 8C, held for 2 min, then increased at 10 8C min21 to 320 8C and held for 10 min. TPH concentration was calculated as area relative to the internal standard peak area. 2.8. Data analysis All multivariate analyses were carried out in Simca-P 8.0 (Umetrics, Umea˚, Sweden). PCA was carried out for both PLFA and Biolog profiles on mean-centred normalized data (i.e. data expressed as a percentage of the average value of the set) using the covariance matrix. The normalized PLFA data were log-transformed prior to PCA, but not otherwise
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scaled. This has the effect of downweighting the relative importance of the PLFAs present in high concentrations, which would otherwise dominate the PCA. However, because the data are not transformed to unit variance (in other words, not analysed using the correlation matrix), the ‘information’ as to which PLFAs are more biochemically and ecologically important, i.e. which are found in higher quantities, is still preserved within the dataset. This is also an unbiased way of downweighting the relative importance of PLFAs present in very low concentrations—which tend to have higher variances because of analytical uncertainty, and can thus unrealistically affect PCA when data are scaled to unit variance. Further details of manipulation of data from sole C source utilization tests prior to PCA are given above. The CLPP and PLFA data were further analysed at individual sampling times using partial least squares discriminant analysis (PLSDA), in order to distinguish treatment-related changes from changes caused by incubation of the soil. The reader is referred to the relevant literature for a full description of partial least squares analysis (Eriksson et al., 1995). Briefly, PLSDA is a ‘supervised’ technique, unlike PCA, meaning that the samples are assigned to classes based on prior knowledge (in this case, experimental treatment). Axes are fitted to a dataset as for PCA, but in partial least squares analysis the fitting algorithm is calculated both to explain a high proportion of the variance within the data, and also to maximize correlation with an external variable or variables. Assigning a class variable, or factor, to samples according to experimental treatment results in PLSDA. Adding more axes to a PLSDA model will increase the apparent amount of data explained, but too many axes will overfit the model and give spurious results. Care should be taken to avoid overfitting. Cross-validation is a simple and powerful technique for doing so: the data are divided into a number of groups at random, and each group in turn is omitted from the data and the analyses re-run. The predicted value for each omitted data point can then be subtracted from the actual value to generate a set of ‘predictive residuals’. These can be used to produce the q2 summary statistic, analogous to the r 2 statistic (Eriksson et al., 1997):
then cleared by centrifugation at 3000g for 30 min. The strains used were V. fischeri (the Microtoxw acute assay, commonly referred to as the ‘luminescent bacteria test’) and the hydrocarbon degrader P. putida F1 (pUCD607) (H.J. Weitz, Unpublished Thesis, University of Aberdeen, 2000). V. fischeri is naturally bioluminescent, whereas the P. putida strain has been marked with the multi-copy plasmid pUCD607, which contains the constitutively-expressed luxCDABE cassette. Freeze-dried cultures were used for both strains. All bioassays were carried out in triplicate, and light output measured using a Jade portable luminometer (Labtech, Uckfield, UK). Full experimental details are reported in Bundy et al. (2001); a brief summary is given here. The luminescence was compared to that of control soil extracts and expressed as percent inhibition. V. fischeri cell suspensions (10 ml) were added to 100 ml methanol extract, 100 ml 22% (w/v) NaCl solution, and 790 ml deionized water. Luminescence was read after 15 min. P. putida F1 (pUCD607) cells were rehydrated in 10 ml 100 mM KCl for 60 min, then 100 ml cell suspension added to 100 ml methanol extract and 800 ml 113 mM KCl, i.e. such that the final KCl concentration in the cuvette was 100 mM. Luminescence was read after 30 min.
3. Results 3.1. Removal of oils The loss of oils as demonstrated by decline in TPH showed (Fig. 1) that paraffin was removed more rapidly and to a greater extent than motor oil, with less than 10% remaining by the end of the experiment. More than 50% of motor oil was removed by day 103. The bulk soil microbial variables measured, basal respiration and microbial biomass, indicated that the highest measured biodegradative activity was at day 27 when both respiration and biomass were significantly elevated over control soils ðP , 0:05Þ for both oil types (Fig. 2). The soil pH was unaffected in both motor oil- and paraffin-treated soils ðP . 0:05Þ; but in
q2 or r 2 ¼1 2 ðresidual sum of squaresÞ=ðtotal sum of squaresÞ If standard residuals are used, the r 2 value is obtained. If the predictive residuals are used, the q2 value is obtained. A high q2 value indicates a model with good predictive power that is not overfitted by inclusion of too many axes. 2.9. Bioluminescent bioassays Methanolic extracts were made for biosensor testing. Soil (2 g wet weight) was extracted by sonication for 15 min at room temperature in 4 ml of methanol. The extracts were
Fig. 1. Change in total petroleum hydrocarbon concentration over time. Data are mean ^ SE ðn ¼ 3Þ: Curves represent fitted exponential regressions. A: paraffin. B: motor oil.
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Fig. 2. Response of soil microbial community bulk variables to oil contamination and remediation over time, compared to control soils. A: basal respiration. B: biomass, expressed as total PLFAs. Open circles: controls. Filled squares: paraffin. Filled triangles: motor oil. Error bars represent ^ SE ðn ¼ 3Þ:
the control soils dropped significantly from pH 5.9 at the beginning of the experiment to pH 5.5 by day 62 and pH 5.1 by day 103 ðP , 0:001Þ: 3.2. Toxicity to bacteria The two bioluminescent bioassay organisms employed gave quite different responses to the two oils and the progress of bioremediation. P. putida F1 (pUCD607) was more sensitive to paraffin than to motor oil, with the luminescence of the treated soil extracts below 50% of the control soils at all times (Fig. 3). The maximum toxicity was observed at day 27 for both oils. V. fischeri (Microtox; the luminescent bacteria test) had an opposite response, with paraffin being less toxic, actually stimulating light output at day 1. Luminescence inhibition increased until day 62, and had been partially reversed by the end of the experiment. Motor oil was much more toxic at all times, but also showed maximum inhibition of luminescence at day 62. 3.3. Effects on Biolog CLPPs PCA is an unsupervised technique that is an aid to graphical visualization in changes in the data. It is an appropriate method for looking at natural patterns in data.
Fig. 3. Toxicity during course of bioremediation shown by response of luminescent bioassays to contaminated soil extracts. Error bars represent ^ SE ðn ¼ 3Þ: A: paraffin contaminated soils, P. putida F1 (pUCD607) response. B: paraffin contaminated soils, V. fischeri response. C: motor oil contaminated soils, P. putida F1 (pUCD607) response. D: motor oil contaminated soils, V. fischeri response.
A scores plot of PC 1 against PC 2 for the CLPP data (Fig. 4) shows that the main source of variation within the data is the effect of incubation time: the early samples (from days 1 and 27) have low PC 1 scores, whereas the later samples (from days 62 and 103) have high PC 1 scores. There was a statistically significant ðP , 0:0001Þ correlation of 0.65 between sample time and PC 1 scores. The control soil profiles shifted within principal component space as a function of incubation time as least as much as the contaminated samples. There were no consistent or interpretable changes that could be ascribed to the C sources which were responsible for the differences observed (data not shown). PLSDA is a supervised method, and thus presents systematic differences between sample classes. For all of the CLPP and PLFA models described here, the summary statistics (r 2 ; q2 ; and number of significant components fitted) are given in Table 1. For the day 1 data no significant components could be fitted, indicating that the treatments
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Fig. 4. PCA scores plot of CLPP data, all time points analysed simultaneously. Circles: controls; Squares: paraffin; Triangles: motor oil. Size of symbol represents sampling date: smallest symbols ¼ day 1, largest symbols ¼ day 103.
had not affected the physiological profiles. For the day 27 data, only one PLS component was significant (Fig. 5), separating the control from the treated samples. For the day 62 data, two significant PLS axes can be fitted. The controls are clearly separated along PLS axis 1, whereas the two oil types are separated along PLS axis 2. By day 103, the end of the experiment, once more no significant components could be fitted.
the dataset. However, there was also a shift in control soil community profiles along PC 2 as an effect of incubation time. The control soil profiles had positive PC 1 scores at all sampling dates. The oil-contaminated soils had positive PC 1 scores on day 1, which were not significantly different from those of the control soils on day 1. By day 27, however, all the oil-contaminated soils had negative PC 1 scores, which remained negative and significantly different from the controls for the rest of the experiment. PLSDA was again used to visualize specific differences between groups of samples. As for the CLPP data, no model could be fitted to the day 1 data. Again, as for the CLPP data, the day 27 data were fitted by a model with one significant component, with controls separated from oiled soils along axis 1. For both the day 62 and the day 103 data, a two-component model could be fitted, which separated control from oiled soils along axis 1, and separated the two oil types along axis 2 (Fig. 7). Because of the apparent similarity of the days 62 and 103 data, the data from these two time-points were reanalysed in a single PLSDA model. A scores plot and a loadings plot for this model are both given in Fig. 8. The scores plot confirms that the control soils are separated from oiled soils along axis 1, whereas the two oil types are separated along axis 2. Inspection of the loadings along axis 1 (Fig. 8) shows that the control soils are associated with PLFAs 12:0, 18:0 (10Me), 17:0ai, 17:0br, and 19:0cy, and the oiled soils are associated with 18:1v9, 15:v0, 17:16, and 18:2v6,9. Inspection of the loadings along axis 2 shows that the paraffin-contaminated soils are associated with 15:0 and 17:1v8, and the motor oil-contaminated soils are associated with 18:2v6,9 and 15:0ai.
3.4. Effects on PLFA profiles All the samples contained a variety of PLFAs composed of saturated, unsaturated, methyl-branched, and cyclopropane fatty acids. In all 37 PLFAs with chain length from C12 to C20 were identified and used in the PCA. There was a clear effect of oil treatment along PC 1 (Fig. 6), i.e. the applied treatment was the major source of variation within
4. Discussion 4.1. Disappearance of oils The disappearance of the different oils as seen in this experiment (Fig. 1) may be by a combination of losses
Table 1 q2 and r 2 values calculated from partial least squares analysis for PLFA and CLPP experimental data Samplea
No. of components
CLPP 1 CLPP 27 CLPP 62 CLPP 103 PLFA 1 PLFA 27 PLFA 62 PLFA 103 PLFA 62/103
0 1 2 0 0 1 2 2 2
q2 First
q2 Second
q2 Cumulative
r 2 First
r 2 Second
r 2 Cumulative
0.36 0.27
0.48
0.62
0.46 0.49
0.35
0.85
0.31 0.42 0.32 0.43
0.73 0.43 0.48
0.84 0.61 0.7
0.5 0.49 0.44 0.49
0.47 0.33 0.35
0.96 0.77 0.84
q2 and r2 values represent summary statistics for predictive power and goodness of fit, respectively (Eriksson et al., 1997). A positive q2 value indicates that the PLS model has not been overfitted. a Number following the sample code represents day of sampling.
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Fig. 5. PLSDA scores plot of CLPP data, analysed for each time point separately. Circles: controls; Squares: paraffin; Triangles: motor oil. A: day 27 data; note that only axis 1 is significant, and axis 2 is included only to make the data easier to visualize. B: day 62 data.
through abiotic processes, primarily volatilization, and through biodegradation. No attempt was made to judge the fraction lost through abiotic processes, for example by internal biomarkers (Bragg et al., 1994; Wang et al., 1998), which would be necessary to judge bioremediation efficiency. Our study, however, was intended to test the relative response of microbial variables to a realistic bioremediation model, including both degradation and abiotic losses. The increase in microbial biomass and in basal respiration (Fig. 2) with the concomitant decrease in TPH is evidence that an active hydrocarbon-degrading community had developed during the experiment. As might have been expected due to its higher proportion of lighter fraction hydrocarbons, the paraffin was degraded more quickly and to a greater extent than the motor oil.
Fig. 6. PCA scores plot of PLFA data, all time points analysed simultaneously. Circles: controls; Squares: paraffin; Triangles: motor oil. Size of symbol represents sampling date: smallest symbols ¼ day 1, largest symbols ¼ day 103.
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Fig. 7. PLSDA scores plot of PLFA data, analysed for each time point separately. Circles: controls; Squares: paraffin; Triangles: motor oil. A: day 27 data; note that only axis 1 is significant, and axis 2 is included only to make the plot easier to visualize. B: day 62 data. C: day 103 data.
4.2. Difference between oil types In a previous study we compared Biolog CLPPs with PLFA profiles for the assessment of the effects of diesel in three different soil types (Bundy et al., 2002). Diesel treatment elicited large multivariate changes for both techniques, and demonstrated that, counter-intuitively, the CLPP and PLFA profiles of microbial communities in
Fig. 8. PLSDA of PLFA days 63 and 102 data. A: scores plot. Circles: controls; Squares: paraffin; Triangles: motor oil. B: loadings plot. Individual PLFAs which are influential in the PLSDA separation (defined in this case as [(axis 1 loading)2 þ (axis 2 loading)2]1/2 . 0.2) are labelled directly on the plot.
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the different soil types became less similar following diesel contamination. We have also used lux-marked bacterial bioassays to assess the toxicity of soils contaminated with crude oils (Bundy et al., 2001). In this study, we tested if combining multivariate profiling techniques (PLFA analysis, CLPPs) with bacterial bioassays for general toxicity gave complementary information that could potentially be useful for following the course of remediation at a contaminated site. 4.2.1. Toxicity Previous microbial toxicity studies have shown an initial increase in toxicity as an initial phase of bioremediation, followed by a recovery to non-toxic levels (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). The results of our study are not so clear. Out of the four possible oil/bioassay combinations, three showed some recovery, i.e. decrease in toxicity, by the last sampling time (Fig. 3). Only the response of V. fischeri to motor oil showed no recovery at all, with this combination giving the highest degree of inhibition (. 90% at days 62 and 103). Clearly, our simulated treatment neither completely removed TPHs from the soil, as judged by chemical analysis, nor entirely ameliorated the toxicity, as judged by the bioassays. A more vigorous bioremediation treatment (e.g. including additional aeration or mixing of soil) might have led to a decrease in toxicity, as recorded in previous studies (Wang and Bartha, 1990; Shen and Bartha, 1994; Riis et al., 1996). The original hydrocarbons in the refined oils are likely to act purely through a non-specific narcosis mechanism of action, by partitioning into cell membranes (Van Wezel and Opperhuizen, 1995). The degree of toxicity is related to their hydrophobicity ðlog Kow Þ; although very hydrophobic compounds may not be soluble enough to elicit a toxic response under the bioassay conditions used (Bundy et al., 2003). Furthermore in soil, very hydrophobic compounds are likely to be sequestered into soil organic matter or into oil droplets (Tang et al., 1998), and hence may not exert a toxic effect. Consequently, it has been observed for hydrocarbons that the initial degradation products, which usually are carboxylic acids (Leahy and Colwell, 1990), are more toxic than the parent hydrocarbons (Long and Aelion, 1999). This may well be entirely due to their increased solubility and hence availability. A second possible mechanism is that the indigenous microbes reacted to the presence of these oils by producing biosurfactants, and that the presence of the biosurfactants increased the toxicity of the hydrocarbons by rendering them more available (Mihelcic et al., 1993). This second alternative seems less likely than the accumulation of initial degradation products, simply because the methanol extraction step would tend to reduce the influence of biosurfactants on extractability, certainly when compared to an aqueous extraction. It was unexpected that the two microbial bioassays gave apparently opposite results for judging the toxicity of the two refined oils—paraffin was more toxic than motor oil to
P. putida F1 (pUCD607) at all sampling times, whereas the converse was true for V. fischeri. The precise reason for this is unknown, but may be related to the constraints of the experimental system. For example, we have observed (for a similar lux-marked bioassay strain) that there is a threshold of hydrophobicity above which compounds appear to be non-toxic, even in the presence of a co-solvent (Bundy et al., 2003). Motor oil contains much heavier, more hydrophobic compounds than paraffin, and might thus exert less of an effect on P. putida F1 (pUCD607). In comparison, V. fischeri is sensitive to compounds of high octanol:water partition coefficient (log Kow values . 5; Hermens et al., 1985). However, this does not explain why P. putida F1 (pUCD607) is more sensitive to paraffin than V. fischeri, and is evidence that relying only on a single-species bioassay test is not likely to give reliable results. It is evident that microbial luminescent bioassays alone do not provide an unequivocal means of following the progress of disappearance of the refined oils. We hypothesized that a combination of different community-level techniques with the toxicity assays would provide a more complete picture. The PLFA analysis and CLPP together confirm that the microbial communities were affected as a result of the oil treatment, with treated communities completely separated within principal component space by day 27. As for the toxicity bioassays, the multivariate techniques did not provide clear evidence that there was any ecological recovery of the communities by the end of the experiment: the CLPPs did not distinguish controls from oiled soils, i.e. indicating that there was recovery, but the PLFAs showed that there were still clear differences. 4.2.2. Development of different communities The luminescent bioassays demonstrated that there were some apparent differences between the microbial responses to the two refined oil types. The community-level profiling techniques are much more powerful in being able to discriminate these effects, and are also a direct measure of the indigenous communities, or at least a subset of the culturable indigenous bacteria in the case of the CLPP. Multivariate data analysis allows one to extract information that would not be obvious or easy to obtain by other methods. For example, it is clear that the initial response to both paraffin and motor oil was a generalized response, i.e. was not specific to oil type, even though the two oils have very different physical properties and hydrocarbon compositions. For both of the multivariate techniques, CLPP and PLFA profiling, PLSDA gave only one significant component by day 27 (Figs. 5 and 7). This is a strong indication that the initial community response to oil contamination was nonspecific, probably both through selection for very similar generalist microbes (Atlas et al., 1991) and through general toxic effects of the oils on the community. However by day 62, the paraffin- and motor oil-contaminated soil communities had diverged, as shown by both PLFA and CLPP.
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These differences between the oils were possibly caused by gradual development of more specialist communities, which were able to degrade the different hydrocarbons found in the different oil types. These oil-induced community differences clearly persisted for the duration of the experiment as shown by the PLFA data, which separate the samples at days 62 and 103 by oil type along axis 2 (Fig. 8). It is interesting that the individual fatty acids associated with the paraffin-affected community are 15:0 and 17:1v8, while the fatty acid most highly associated with the motor oil affected community is 18:2v6,9, generally considered to be a marker for fungi in soil (Pennanen et al., 1998). Possibly, the fungi were more able to degrade the aromatic hydrocarbons that would be found in motor oil. It has also been found that linear hydrocarbon (alkane) contamination may directly alter microbial lipid profiles, by becoming incorporated into the PLFAs (Doumenq et al., 1999, 2001). This might perhaps explain the appearance of 15:0 as a fatty acid marker discriminating paraffin from motor oil—the motor oil that we used did not contain any n-alkanes as short as 15 C atoms, as shown by the GC – FID traces, whereas paraffin would likely have contained such short- to medium-length n-alkanes. In contrast, the CLPPs are separated according to oil type within the first two PCs for the day 62 data only, and by day 103 there were no significant effects at all. It is likely that the C substrates in the Biolog GN plates are only able to distinguish broadly between normal and fast-growing, hydrocarbon-degrader-enriched communities. It is still possible that if more suitable C substrates relevant to hydrocarbon degradation could be chosen, or if the assay conditions were changed (Campbell et al., 1997), or whole soil used instead of soil extracts (Campbell et al., 2003) then this might improve discrimination between different oil types by CLPPs. However, an earlier attempt to use carboxylic acids as C sources ecologically relevant to refined oil degradation failed to show any improvements over the standard Biolog GN carbon sources (Bundy et al., 2002). 4.3. Incubation effects in control One of the major influences on the microbial communitylevel responses was the effect of incubation time on the microbial PLFA and CLPP profiles in the control soil. For the CLPPs in particular, this was the single biggest effect on the community profiles, with samples separated by incubation time along PC 1 (Fig. 4). The effect was also noticeable in the PLFA profiles, although in this case, oil treatment gave the largest effect. This incubation effect made it difficult to properly assess the progress of oil disappearance using CLPPs alone, and the effects of oils on the PLFA profiles were certainly clearer. The precise reason for the changes observed in the CLPPs and PLFA profiles with time are not known, but the microbial community of soils is known to be affected by sampling and storage
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(Petersen and Klug, 1994). In our investigation, there was a decrease in soil pH in the control but not in the oiled soils, which clearly indicates that there were time-related changes. These pH-related effects parallel the changes observed in the PLFA profiles, where the main change is caused by oil treatment (separation along PC 1), but there is also a clear separation of the control soils at the beginning and at the end of the experiment, orthogonal to the effects of the oil (Fig. 6). 4.4. Advantages/disadvantages of selected techniques The techniques we used have been applied separately to assess the effects on soil microbes of oil contamination and bioremediation. PLFA community profiling has probably been the most widely used technique, and only a couple of instances will be mentioned here. Stephen et al. (1999) compared PLFA profiles with PCR-DGGE for samples taken from a JP-4 fuel contaminated site. They found that PLFAs characteristic of sulphate-reducing bacteria were more common around the fringes of the spill, and that trans/ cis ratios were higher within the spill, indicating higher amounts of physiological stress. Macnaughton et al. (1999) also used PLFA profiling, as well as 16S rDNA community profiling using DGGE, to follow the effects of crude oil contamination on beach sands. Some recovery of PLFA profiles was observed, but the changes were dominated by a time effect, ascribed to eukaryotic PLFAs derived from invertebrate eggs found in the sands. This and our own study suggest PLFA community analysis can therefore be used as a profiling technique to follow the effects of oil contamination on soil microbes, and has the great advantage of not relying on growth. However, it may not be ideal as a sole technique, as many of the PLFAs are common to many different microbial taxonomic groups, and it is therefore difficult to relate changes to specific members of the microbial community (White et al., 1998). CLPPs based on Biolog microplates have also been used for assessing oil pollution. Wu¨nsche et al. (1995) compared remediation of a contaminated refinery soil with a spiked pristine arable soil. The largest effect they observed was that the spiked soil CLPPs changed much more than did those of the refinery soil. Lindstrom et al. (1999) examined the site of an aged oil spill at Arctic latitudes. They took the novel and interesting approach of comparing the multivariate response of inocula of different dilutions of the extracted soil bacteria. The number of substrates utilized by the control site samples was reduced more at high dilutions than for the contaminated samples, implying that there had been an increase in metabolically versatile, generalist bacteria at the contaminated site. This might be expected as it has been suggested that oil contamination reduces microbial biodiversity, but increases catabolic diversity by selecting for competitive, generalist type strains (Atlas et al., 1991). The CLPP approach has been strongly criticised due to its dependence on culturing (Konopka et al., 1998).
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Nonetheless, it is still likely that useful information on the subset of culturable bacteria can be obtained using this method (Garland, 1997) especially if hydrocarbons do select for fast-growing bacteria (Kozdro´j and van Elsas, 2000). The naturally luminescent bacterium V. fischeri (the Microtox acute assay) has been used several times for following the course of bioremediation, and was included here to permit comparison with previous work. However, it is a marine organism, and highly sensitive to physical factors such as osmotic strength, and so its response might not be appropriate for monitoring terrestrial pollution. For example, apparent toxicity to V. fischeri was shown to increase considerably in the presence of soil extract, whereas the same effect was not seen with a soil organism (Bundy et al., 1997). P. putida F1 (pUCD607) is a hydrocarbon-degrading soil organism, and therefore highly ecologically relevant to studies of oil contamination and bioremediation.
5. Conclusion The different oil types, the light paraffin and heavy motor oil, increased the toxicity to soil microbes compared to control soils, but there were indications that this toxicity was ameliorated as bioremediation progressed. The use of community level techniques provided totally different information—thus it was evident that the initial response of the soil community to oil contamination was general, with no distinction between motor oil and paraffin, but that with the passage of time more specialist communities developed. Thus the use of community profiles and singlespecies toxicity bioassays do provide complementary information to assess the mechanisms and events associated with the recovery of soils contaminated by oil hydrocarbons. Oil-induced changes were more clearly shown by the PLFA profiles, as the CLPPs were very sensitive to changes caused just by incubation of the soils.
Acknowledgements J.G. Bundy was funded by the award of an Aberdeen Research Consortium PhD studentship. C.D. Campbell was funded by the Scottish Executive, Environment and Rural Affairs Department.
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