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Community structure and degradation potential in bioremediation systems treating contaminated soils
Von der Fakultät für Chemie und Physik der Technischen Universität Bergakademie Freiberg genehmigte
DISSERTATION
zur Erlangung des akademischen Grades doctor rerum naturalium Dr. rer. nat.
vorgelegt von Dipl.-Geoökol. Nicole Popp geboren am 18.10.1977 in Karl-Marx-Stadt (Chemnitz)
Gutachter: Prof. Dr. Michael Schlömann; Freiberg Prof. Dr. Anton Hartmann; Neuherberg Prof. Dr. Ulrich Szewzyk; Berlin
Tag der Verleihung: 02.06.2006
Für René und Klein-Fabienne
Ihr müsst Eure Kinder lehren, dass der Boden unter ihren Füssen die Asche unserer Großväter ist. Damit sie das Land achten, erzählt ihnen, dass die Erde erfüllt ist von den Seelen unserer Vorfahren. Lehrt Eure Kinder, was wir unsere Kinder lehren: Die Erde ist unsere Mutter. Was die Erde befällt, befällt auch die Söhne der Erde.... (Häuptling Seattle)
VII
Contents
1 General Part...... 1
1.1 Background of this study...... 1 1.2 Classical methods for the detection of the degradation activity in a heap ...... 5 1.3 Molecular methods for the detection of the degradation activity in a heap – Taxonomic aspects...... 9 1.4 Molecular methods for the detection of the degradation activity in a heap – Physiological markers ...... 20 1.5 Concluding remarks ...... 28 2 Microbial diversity in the active stage of a bioremediation system for mineral oil hydrocarbon contaminated soils...... 31
ABSTRACT ...... 32 INTRODUCTION...... 32 MATERIALS AND METHODS...... 35 RESULTS AND DISCUSSION ...... 39 ACKNOWLEDGEMENTS...... 53 3 Quantification of bacterial groups and of catabolic genes in mineral oil hydrocarbon contaminated bioremediation systems...... 55
ABSTRACT ...... 56 INTRODUCTION...... 56 MATERIALS AND METHODS...... 59 RESULTS...... 74 DISCUSSION ...... 82 ACKNOWLEDGEMENTS...... 89
References...... 91
Danksagung
1
General Part
1.1 Background of this study
Soil is a sensitive, very complex and heterogeneous environmental compartment, which is not renewable or only within long time periods, respectively. Soil is not an isolated matter, it has to be seen as a part of the biosphere. The extensive human usage of soil comes in conflict with its natural functions and its ability to compensate material entries. So, a great aim of our society has to be the protection and preservation of the soil. In Germany, the adoption of the “German Soil Protection Act” (BBodSchG) in 1998 was a first step to approach this problem by federal legislation. The focus on what had happened to soil, was then more intensive than before and soil was defined as subject of protection.
The contamination of the soil with mineral oil hydrocarbons is a widespread environmental problem. Normally, mineral oil hydrocarbons are ubiquitous in the environment and can even be produced by organisms (Gallego et al., 2001; van Beilen et al., 2003). But the enormous consumption of petroleum, coal, petrol, diesel, and related products led and still leads to a contamination of soil caused by inappropriate transport, storage and treatment processes for example by oil tank storage areas, oil refineries and tar factories. The original material of all mineral oils is crude oil, which is divided into gas (C1-4), petrol (C5-12), medium distillates (C10-22), vacuum gas oils (C20-40) and residues (C19-90) (Hellmann, 1995). The chemical composition and the physical properties of crude oil vary with its geographical origin. Processing factories of brown coal are one reason for contaminated soil, especially in Central Germany. Brown coal was mostly used for the production of electricity and tar. As an example, the crude coal of Rötha near Leipzig (Böhlen-Espenhain region) consists of 54 % water, 4 % ashes and 42 % combustible matter, which contains 70 % carbon, 6 % hydrogen, 21 % oxygen and nitrogen as well as 3 % sulphur (Kretschmer, 1997). 2 Background of this study
To use contaminated soil in later applications, at least for road constructions, for industrial areas or for recultivation, a remediation is necessary in most cases. Often bioremediation is the applied strategy (Juteau et al., 2003). This biological process of mineralisation or transformation metabolises organic compounds to
CO2 and water or to less toxic forms (Plaza et al., 2003). For this purpose usually the degradation potential of the indigenous microflora is exploited. The microbial degradation is influenced by the bioavailability of the contaminating products, the availability of nutrients and oxygen as well as the soil properties (Friedrich et al., 2000; Volkering et al., 1998).
Many microorganisms are able to degrade mineral oil hydrocarbons under laboratory and field conditions but generally only with a relatively small substrate range (Ratajczak et al., 1998; Wikström et al., 1996). However, mineral oil hydrocarbon contaminated soils consist of a broad spectrum of various compounds. This requires a consortium of various degrading microorganisms, which is adapted to the environmental conditions, like temperature, moisture, nutrient- and oxygen supply. Furthermore, in most cases contaminations are creeping, which allows the adaptation of the soil flora over long time periods. A laboratory strain or a mixed culture with degrading potential does not meet these requirements. So laboratory and field experiments have mostly shown that bioaugmentation is not reasonable for such a contamination (Jørgensen et al., 2000; Margesin & Schinner, 1997).
Many strategies exist for the remediation of mineral oil hydrocarbon contaminated soils. During in-situ remediation the soil body will remain largely unaffected. The elimination of the contaminants may be achieved by exchanging the soil air or by pump-and-treat technology. One of the most applied strategies is the off-site remediation in soil heaps (Fig. 1). The excavated soil material is heaped up and aeration lances are introduced to create aerobic conditions in the bioremediation system (Condé & Hagedorn, 1997). The temperature of the heap may be monitored throughout the technical process. Sometimes, additional mineral fertiliser (NPK) is added to the soil as nutrient supply for the microorganisms to achieve biostimulation (Margesin & Schinner, 1997). Another aspect for biostimulation was described in a study on the influence of earthworms in bioremediation (Schaefer, 2001). In contrast to
Background of this study 3 controls without earthworms, soils with earthworms showed a significant reduction of pollutant concentration. This is possibly an effect of the better aeration of the soil due to earthworm activity resulting in an enhancement of microbial activity.
For reasons of economy, the cleaning of a contaminated soil in practice does not reach the level, at which the contamination is no longer detectable. The aims of the remediation depend on the case of decontamination and on the further application. The assignment criteria Z0 to Z5 of the Länderarbeitsgemeinschaft Abfall (LAGA, Working Group of the Federal States on Waste) describe the approved usage of contaminated soil material (LAGA, 2003; LAGA, 2004a). The assignment criteria Z0 (≤ 100 mg mineral oil hydrocarbons per kg soil dry weight), Z1 (≤ 300 mg mineral oil hydrocarbons per kg soil dry weight) and Z2 (≤ 1000 mg mineral oil hydrocarbons per kg soil dry weight) allow a reuse of the soil under defined conditions.
add mineral fertiliser
temperature sensor ventilation system
soil heap
FIG. 1: Schematic illustration of a ventilated bioremediation system
When investigating the performance of bioremediation systems, it has to be kept in mind that artefacts occur during handling of the contaminated material and they will influence the results. Thus, while on-site the concentration of mineral oil hydrocarbons is often on a very high level, it may no longer be detectable after transport to the bioremediation plant. The soil material is necessarily aerated during excavation and transport. If the contamination 4 Background of this study consists mainly of volatile components, they will exhaust and the soil may be almost clean before reaching the treatment plant. This experience commonly made by owners of bioremediation plants, refers especially to material from oil tank storage areas and filling stations. Therefore, the degradation of BTEX and short-chain alkanes usually plays a minor role in such systems.
Classical methods 5
1.2 Classical methods for the detection of the degradation activity in a heap
A major problem for companies running bioremediation plants is that some contaminated soil materials are easily remediated and others show almost no degradation activity. For optimisation of the technical remediation process it may be helpful that company owners have convincing tools for prognosis of the remediation success. Many parameters, like bioavailability of the pollutants, oxygen- and nutrient supply, moisture, temperature, pH value and heavy metal content of the soil, influence the degradation process. It is hardly possible to draw conclusions from one of these parameter on the absence of the degradation or the unsatisfactory degradation. A successful process control and regulation requires the analysis of many parameters.
The degradation of the pollutants is commonly detected by the measurement of mineral oil hydrocarbon concentration in the soil material by means of chemical analyses (LAGA, 2004b). The samples, which are necessary for these analyses, are usually taken at time points, which specified by the owner based on experience with this kind of degradation process.
During the bioremediation process the temperature increases inside the bioremediation heap, similar to a compost pile. This reflects the degradation activity of the indigenous microflora, which use mineral oil hydrocarbons as carbon and energy source. Thus, the temperature pattern of a heap com is measured as a matter of routine by some companies. If the bioremediation system no longer shows a significant temperature increase and the process already took a long time, without having reached the remediation aim, soil material will be turn over again. However the assessment of the activity for pollutant degradation by means of temperature inside the bioremediation system is relatively difficult. Such bioremediation plants are simply large halls, which do not allow the creation of a constant “room temperature”. Thus the temperature is largely dependent on environmental conditions. Such a bioremediation system is a slow system and reacts to ambient temperature with a certain delay. For example, in the first cool days of autumn the bioremediation 6 Classical methods system will still have an increased temperature and will react with a delay to a temperature decrease.
Also the particle size, a pedological parameter, allows an approximate assessment of the length of the degradation process after the excavation of soil material. The pore volume of sandy soils is larger than that of loamy and clayey soils, thus the oxygen supply in sandy soils is better and the aerobic degradation is stimulated (Hagedorn 2002; personal communication). Thus the degradation in sandy soils may be faster.
The degradation activity of the bacterioflora can also be detected by soil respiration. The evolution of CO2 is measured with this method (Margesin et al.,
2000b; Winding et al., 2005). But the degradation products are not always CO2 and H2O, also metabolites can be generated (Margesin et al., 2000b). Thus less
CO2 is formed by the incomplete degradation of pollutants, and degradation activity may be underestimated. In contrast, other compounds of the soil matter like glucose and organic matter are also used as nutrients by the microorganisms. The CO2-evolution of these processes are additionally detected and may lead to overestimation of the degradation activity. A similar method to the measurement of CO2 formation, is the detection of oxygen consumption. Not only the aerobic degradation of the pollutants need oxygen, also many competitive reactions, like nitrification, Fe(II) oxidation or aerobic respiration of low-molecular weight compounds (Vogt et al., 2002). So the oxygen demand for the degradation of the pollutants is overestimated.
Some investigations tried to correlate the soil enzyme activities with the degradation of mineral oil hydrocarbons (Del Panno et al., 2005; Margesin et al., 2000a; Margesin et al., 2000b; Plaza et al., 2003). A fresh contamination of fertilised soils with diesel oil resulted in an increased dehydrogenase, lipase and urease activity (Bakermans & Madsen, 2002; Margesin et al., 2000b). Such an increase was only observed in soils with active alkane degradation and not in soils contaminated with polycyclic aromatic hydrocarbons. The dehydrogenase activity also increased directly after a contamination of soils with petrochemical sludge consisting of 60 % polycyclic aromatic hydrocarbons and 10 % n- alkanes (Del Panno et al., 2005). It was not investigated, if the increase of the soil enzyme activities can also be observed in aged contaminations. Therefore
Classical methods 7 an uncontaminated reference sample would be necessary. But for technical reasons, this will be only applicable for smaller contaminated areas like filling stations. The soil enzymes are also involved in other degradation processes in the soil. It is therefore difficult to differentiate which portion originates from the degradation of pollutants and which from other soil processes. To eliminate this, a reference sample is necessary, but such a sample is often difficult to be obtain for aged contaminations.
The methods specified above provide no information on the microbial diversity and the degradation potential in such contaminated soils or in such bioremediation systems. Cultivation experiments are one possibility for this purpose. The analysis of the microbial community in contaminated soils by means of cultivation was investigated in many past studies. Some results of this investigation strategy are reviewed in the next section. The genera Pseudomonas and Acinetobacter dominated the microbial community in desert soils contaminated with crude petroleum oil (Saadoun, 2002). Another study about oil-polluted desert soils reported that only few genera predominated the soil samples, namely Bacillus, Pseudomonas, Streptomyces and Rhodococcus (Radwan et al., 1995). However, the relative predominance of individual genera varied seasonally and was dependent on the type of management. Plaza et al. (2003) investigated heaps of soil heavily contaminated with petroleum waste. Here, the bacterioflora was dominated especially by the genera Pseudomonas as well as Chryseomonas, Sphingomonas and Stenotrophomonas. Alcaligenes spp. and Rhodococcus spp. were abundant in a soil contaminated with polycyclic aromatic compounds (La Rosa et al., 2006). The microbial community of diesel-contaminated soils treated in a bioreactor was dominated by Acinetobacter spp. and Pseudomonas spp. (Gallego et al., 2001). The cultures of hydrocarbon-contaminated Antarctic soil were determined to be largely Actinobacteria and Alphaproteobacteria (Saul et al., 2005). Similar results were also obtained in further investigations (Greene et al., 2000; Jirasripongpun, 2002; Mueller et al., 1997). Thus, pseudomonads and Actinobacteria dominate the microbial community in contaminated soil samples, when the community structure is analysed by cultivation methods. But only circa one percent of the microorganisms occurring in nature can presently be cultivated by common methods (Torsvik et al., 1990). Furthermore the established cultivation methods 8 Classical methods favour the isolation of fast growing microorganisms which often prefer high nutrient concentrations (Felske et al., 1999). The real microbial diversity in the soil is not reflected in this way.
Most of the methods mentioned above were performed and discussed in the study of Jørgensen et al. (2000) on the bioremediation of petroleum hydrocarbon-contaminated soil in biopiles. All these methods give insufficient information on the microbial diversity and the degradation potential in a bioremediation heap. Thus the biodegradation in soils is dependent on many factors and multiple parameters are necessary for the assessment of the remediation success. Furthermore, additional parameters, which may be obtained e.g. by molecular methods, could help to resolve this problem. Cultivation-independent methods are able to reflect the composition of the microbial community in a better way than cultivation-dependent methods. Though nucleic acid extraction methods, PCR conditions and used primers as well as cloning procedures may still influence the diversity analysis, they probably do so to a considerably smaller extent than cultivation (Kirk et al., 2004; Kozdroj & van Elsas, 2001).
Taxonomic aspects 9
1.3 Molecular methods for the detection of the degradation activity in a heap – Taxonomic aspects
Which microorganisms could be detected in soils contaminated with mineral oil hydrocarbons? As cultivation is not sufficient to detect the whole microbial community (Torsvik et al., 1990), molecular methods could be used to investigate this aspect in a better way. Taxonomic analyses focused on the microbial diversity in soils contaminated with mineral oil hydrocarbons is described in some studies (Juck et al., 2000; Kaplan & Kitts, 2004; Mills et al., 2003) based on DNA level.
Kaplan & Kitts (2004) investigated the bacterial succession in a petroleum land treatment unit, which is comparable to the heaps in this study. The petroleum weathered after up to 30 years in the soil in the study of Kaplan & Kitts (2004). Two clone libraries were analysed at different points during the degradation process. At day 14 Flavobacterium spp., Pseudomonas spp. and Azoarcus spp. dominated the clone library. At day 56 a change of the community structure was detected, then Alcaligenes spp., Microbacterium spp. and Bacteroides spp. were prevalently. Mills et al. (2003) also investigated the bioremediation of petroleum-contaminated soils. However, in this case the soil was treated in a bioreactor amended with inorganic nutrients. The genera Pseudomonas and Sphingomonas dominated the community. Cold-adapted bacterial communities in petroleum hydrocarbon-contaminated soils with no degrading activity were investigated by Juck et al. (2000). In contrast to the other studies Actinobacteria dominated the bacterioflora. However, also Gammaproteobacteria and Alphaproteobacteria could be detected, sphingomonads were present but surprisingly no pseudomonads were detected. Saul et al. (2005) analysed the bacterial diversity in an Antarctic hydrocarbon-contaminated soil. The community was dominated by Sphingomonas spp., Variovorax spp., Pseudomonas spp. and Actinobacteria.
The analysis of a microbial community on SSU rDNA, where active as well as inactive bacteria are detected, demonstrate the genetic potential (Lüdemann et al., 2000). The active portion of a microbial community is obtained by analysing the SSU rRNA, because it is directly involved in the active protein synthesis 10 Taxonomic aspects
(Morgan et al., 2002; Trevors, 1996a). One aim of this study, was the detection of the active bacterioflora in a bioremediation system, and that is why the SSU rRNA was used for the investigation of the microbial community composition. The metabolically active members of a bacterial community in a polychlorinated biphenyl-polluted soil with no degrading activity were identified by Nogales et al. (1999). But, to my knowledge, the present study is the first attempt to analyse the SSU rRNA in a remediation system.
Three bioremediation heaps of soil material from Rositz (Altenburger Land; Thuringia) were used as the model systems and were sampled during the whole technical degradation process. The Rositz tar factory processed brown coal tars, creosote and petroleum between 1917 and 1990 (Heine et al., 1999; Verwaltungs- und Verwertungsgesellschaft Industriegelände Rositz mbH, 1998). The brown coal came from the Böhlen-Espenhain area. In Rositz, brown coal tar was processed by destructive tar destillation (Schmiers, 2001). This process is based on the principle of thermal cracking under pressure at a temperature between 430 and 455 °C. Mainly purest paraffins were produced from tar oils. The carbon composition of the used brown coal tar consisted of 42 % paraffin carbon, 40 % naphthalene carbon and 19 % asphalthene carbon (Schmiers, 1973). Bombardments in World War II were the main reason for the extensive aged contamination in this area. The contamination of soil and ground water consisted of alkanes, BTEX- and polycyclic aromatic compounds as well as phenols. The soil of the three Rositz heaps contained different amounts of mineral oil hydrocarbon (Rositz 1: 2000 mg mineral oil per kg soil dry weight; Rositz 2: 4000 mg mineral oil per kg soil dry weight; Rositz 3: 8000 mg mineral oil per kg soil dry weight). Figure 2 shows the bioremediation heap Rositz 3 with the ventilation system.
Additional samples from the beginning of the bioremediation process of two other bioremediation heaps were also investigated. The oil tank storage areas Grimma (2900 mg mineral oil per kg soil dry weight) and Espenhain (9600 – 15200 mg mineral oil per kg soil dry weight) were located in former Russian military bases in Saxony.
Taxonomic aspects 11
FIG. 2a: Front of the bioremediation system Rositz 3.
FIG. 2b: Top of the bioremediation system Rositz 3. It is shown, how the ventilation system is introduced into the bioremediation system. 12 Taxonomic aspects
The SSU rRNA used for the taxonomic analysis were obtained by an extraction method, which allows the simultaneous extraction of RNA and DNA from soil samples (Görres, 2001) and a separation of the nucleic acids afterwards. The extraction method allows the triple treatment of the soil with glass beads. For further investigations, the extract after the first bead treatment was separated from the extracts of the second and third bead treatments, to filter possible differences of the sequential extraction. The extracted RNA was used for reverse transcription (rT)-PCR and the cDNA was used for creation of two clone libraries.
The phylogenetic distribution of the clone libraries from the active bioremediation heap Rositz 3 is shown in Figure 3 and is described in detail in the second part of this dissertation. The clone library was dominated by the Gammaproteobacteria. Members of the Alphaproteobacteria and Betaproteobacteria were represented in the same ratio in the library, but only to one third of the Gammaproteobacteria. Additionally, members of the Epsilonproteobacteria, Bacteroidetes, Actinobacteria and Firmicutes were detected. Cultivable representatives are described for all these genera and in many cases degraders of mineral oil hydrocarbons are known for the detected genera (Mills et al., 2003). However the Rositz sequences clustered together in the phylogenetic tree and most similar sequences usually originated from soils.
The dominance of gram negative bacteria in particular Gammaproteobacteria in mineral oil hydrocarbon contaminated environments is described elsewhere and has been confirmed by various analysing strategies e.g. by cultivation (Stapleton et al., 2000), by phospholipid fatty acid analysis (PLFA) (Macnaughton et al., 1999) and by cloning and sequencing of fragments obtained by amplicon length heterogeneity PCR (LH-PCR) and (terminal) restriction fragment length polymorphisms ((T)RFLP) (Mills et al., 2003; Saul et al., 2005). It is shown in several investigations, that under high nutrient conditions a “γ shift” is detectable (Amann et al., 1995). This has been also observed in contaminated soils (Chao & Hsu, 2004). The shift of community structure from gram positive to gram negative microorganisms in contaminated soils is a well known effect (Macnaughton et al., 1999; Margesin et al., 2003;
Taxonomic aspects 13
Saul et al., 2005). However, the degradation ability is not only limited to the Gammaproteobacteria (Mills et al., 2003; Vomberg & Klinner, 2000).
Betaproteobacteria
Alpha- proteo- bacteria
Gamma- proteo- bacteria
FIG. 3: Maximum likelihood tree of the genera in which Rositz sequences could be detected.
The Gammaproteobacteria of Rositz 3 consisted of two larger sequence clusters comprising Pseudomonas spp. and Acinetobacter spp. The degradation of pollutants has often been investigated using pseudomonads (Evans et al., 2004). Genus Acinetobacter is detected in diesel contaminated soils (Chao & Hsu, 2004; Gallego et al., 2001). The Rositz sequences belonging to the Alphaproteobacteria were related to the genera Sphingomonas and Zymomonas. Sphingomonads are known for their degradation potential for a variety of pollutants (Mohn et al., 1999; Thiel et al., 2005; Vomberg & Klinner, 2000). Mills et al. (2003) reported the dominance of sphingomonads besides pseudomonads in a bioreactor treating petroleum-contaminated soil. Zymomonas spp. are rather typical for producing ethanol by fermentation of 14 Taxonomic aspects glucose and fructose (Dien et al., 2003). Rositz sequences, which affiliated to the Betaproteobacteria were diverse and evenly distributed in the phylogenetic group. The genera Acidovorax, Rhodoferax and Thiobacillus occurred in particular. They were related to strains or sequences from contaminated or pristine aquatic environments, where members of the Betaproteobacteria dominated (Alfreider et al., 2002; Bakermans & Madsen, 2002; Glöckner et al., 2000; Huber et al., 2003; Lüdemann et al., 2000). The sequencing analysis of bioremediation system Rositz 3 demonstrated that this group may also be prevalent in contaminated soil. The detection of the genus Thiobacillus was surprising, because it is mainly known for autotrophic sulphur oxidation, but some representative can also grow heterotrophically. Strains, which are able to degrade mineral oil hydrocarbons or related substances were also found in the other phyla, which were detected in Rositz 3: the Epsilonproteobacteria (Alfreider et al., 2002; Kodama & Watanabe, 2003), Actinobacteria (Greene et al., 2000; Kaplan & Kitts, 2004) and Bacteroidetes (Kaplan & Kitts, 2004).
The sequential nucleic acid extraction did not result in an obviously different phylogenetic distribution of the sequences at the phylum level. The cell wall structure of gram positives requires harder lysis conditions than gram negative bacteria (Bürgmann et al., 2001). The assumption that a larger portion of the nucleic acid from the second/third bead treatment might originate from gram positive bacteria, could be not confirmed, as the clone libraries from the sequential extracts contained nearly the same portion of gram positives. It is therefore assumed that the nucleic acid of second/third bead treatment derived from the inside of soil particles or aggregates (Ranjard & Richaume, 2001). In contrast to the phylum level, differences between the sequential extractions were observed on the genus level. Acinetobacter and Zymomonas sequences were specific for the second/third bead treatment, whereas Sphingomonas was specifically found in the first bead treatment. Some other sequence types were obtained in both extracts and the sequences often clustered together within their respective kind of extract. An example of this observation are the Rositz sequences similar to genus Acidovorax.
The sequencing analysis are influenced by biases concerning nucleic acid extraction, PCR, primer and cloning. The analysis of microbial communities by
Taxonomic aspects 15
PCR-mediated methods often results in an overestimation of the abundance of certain microorganisms (Watanabe et al., 2000). At the same time nucleic acid fragments, which are difficult to amplify, are often not detected (Watanabe et al., 2000). Also fingerprint methods like TRFLP, DGGE or SSCP, since based on the principle of PCR, may underestimate the diversity of a population (Mills et al., 2003), because there is no clear separation of the single bands in a highly diverse population (Kozdroj & van Elsas, 2001). But it is an utopia to sequence the whole soil meta genome (Greene & Voordouw, 2003). For statistical coverage, it will be necessary to sequence an enormous amount of clones to obtain also rare community members. But as mentioned above these culture- independent methods are expected to influence the diversity analysis less than cultivation experiments (Felske et al., 1999; Kirk et al., 2004; Kozdroj & van Elsas, 2001).
It has been shown that the clone distribution is not the same as the species distribution in an environmental sample and hybridisation experiments are necessary to obtain quantitative information (Fuhrmann et al., 1993). Hybridisation experiments can be performed on membranes (Raskin et al., 1997) or in situ (e.g. fluorescence in situ hybridisation (FISH)) (Amann et al., 1995). As at the moment FISH cannot be applied to soil samples, because of the auto-fluorescence of soil particles and the attachment of bacteria on the humic substances and soil particles (Böckelmann et al., 2003), membrane hybridisation was used in this study and detailed described in the third part of this dissertation.
The selection of appropriate probes is very important for hybridisation. Probes may have different detection limits, some are group or genus specific (Jeon et al., 2003; Schweitzer et al., 2001), other detect individual species (Stoffels et al., 1998). Genus specific probes are most suitable for the purpose here. Several probes have been described for many genera in the literature, e.g. for Pseudomonas spp. (Braun-Howland et al., 1993; Kenzaka et al., 1998; Stoffels et al., 1998), for Acinetobacter spp. (Braun-Howland et al., 1993; Kenzaka et al., 1998; Wallner et al., 1995) or for Acidovorax spp. (Schramm et al., 2003; Schulze et al., 1999). In contrast, no probes are published e.g. for the genera Thiobacillus and Rhodoferax. However, probes can be newly designed by 16 Taxonomic aspects means of sequencing data with specific programme tools (e.g. ARB software (Ludwig et al., 2004)).
The optimisation of the hybridisation conditions, which can focus e.g. on the hybridisation temperature, requires suitable positive and negative control strains. Probes have to match perfectly with positive controls and negative controls should have only one or two sequence mismatches with the probes. In some cases strains for negative control do not meet this criteria. An example is Pseudomonas putida ATCC 12633, which offers the best negative control for the Acinetobacter-specific probe Ac (Kenzaka et al., 1998) with 6 mismatches. Finding positive controls is not a problem for the published probes. However, strains for positive controls are not available for probes developed based on cloned sequences. In vitro transcription can rectify this lack. However, it should be kept in mind that the hybridisation behaviour of in vitro transcribed RNA differs from native rRNA, although the dissociation temperature (Td) is similar (McMahon et al., 1998). An explanation for differences in hybridisation signals with transcripts and native rRNA could be the specific probe target locations, which are differently denaturated in the two RNAs. The authors concluded, when using in vitro transcripts as standard curves in hybridisation experiments, that the environmental microflora might be underestimated in many cases.
Some additional aspects have to be considered when applying the membrane hybridisation strategy on soil samples. Sometimes only weak hybridisation signals of the specific probes are obtained. The problematic measurement of nucleic acid concentration in soil samples is one reason for weak signals. It has been reported that DNA quantification of soil extracts with spectrophotometric analysis is inadvisable, because soil contains strongly absorbing compounds (Trevors, 1996b). It can be assumed that this is also true for RNA quantification. Fluorometric measurements have been suggested as alternative methods, and they were used in this study. However, the fluorometric concentration measurement also shows some limitations. The fluorescent dye RiboGreen for RNA quantification also binds to DNA residues (Jones et al., 1998) and may also bind to humic acids in the extracts. So RNA concentrations might have been overestimated, because of signals which were additionally obtained from DNA and humic acid residues. A further reason for the weak hybridisation signal
Taxonomic aspects 17 is the binding capacity of the membranes. It has been demonstrated that humic substances saturate the membrane so that a lower amount of RNA can be bound, which causes a decrease of signal intensity (Alm et al., 2000). The same effect may result from a high DNA concentration level in RNA extracts. Even traces of DNA reduces the signal intensity because of interactions between DNA and target rRNA (Alm et al., 2000). Furthermore, these weak hybridisation signals are strongly influenced by background signals. So cloudy background on the membrane may lead to overestimation of the hybridisation signals.
In consideration of all the aspects mentioned above, the quantitative distribution of the abundant genera in the active heap Rositz 3 was investigated during the technical remediation process. The heaps Rositz 1 and Rositz 2 as well as the heaps Grimma and Espenhain served for comparison. The obtained data for this aspect are shown in detail in the third part of this dissertation. Probes from the literature were chosen for the abundant genera Sphingomonas (Neef, 1997), Pseudomonas (Braun-Howland et al., 1993), Acidovorax (Huber, 1997) and Acinetobacter (Kenzaka et al., 1998). Additional probes were designed in this study for the genera Thiobacillus and Rhodoferax and the clone TRS13 group.
The quantitative investigation of the different bioremediation heaps showed that a higher portion of the individual genera were detected in the extracts of the first bead treatment than in the extracts of the second/third bead treatment. In all stages of the five studied bioremediation heaps the genera Sphingomonas and Thiobacillus were detectable. Species related to group clone TRS13 were only detectable when probing Rositz soil material, which suggests that these species are specific for the Rositz soil. The detection of Acinetobacter spp. was only successful in bioremediation system Rositz 1. The lack of Acinetobacter in all other samples was surprising, although a few related sequences could be detected in bioremediation system Rositz 3. The probe AC (Kenzaka et al., 1998) might not be suitable for detection of low levels of Acinetobacter spp. in a soil environment. Alternatively, clone library analyses may have originally overestimated the occurrence of this genus.
All three Rositz bioremediation heaps showed a similar distribution pattern of the individual genera. No correlation, however, was shown between the 18 Taxonomic aspects temperature in the heaps and the portion of the detected genera. Bacterial diversity during the active stage of Rositz 3 was investigated in detail. The abundant genera in this diversity study were already at a high level before the technical start of the bioremediation and during the first active stage. Nearly the whole microbial community was detected with this small number of probes. Afterwards a drop of these populations was visible, although the degradation process went on. The turnover of soil material of the heap Rositz 3 also induced only a small increase of the still detectable genera. At the end of the technical bioremediation process more of the specifically detected genera were detectable in the heaps Rositz 1 and Rositz 2 than in the heap Rositz 3. The changing composition of the microbial community during the bioremediation process has been reported also in other studies (Abed et al., 2002; Greene et al., 2000; Kaplan & Kitts, 2004; Macnaughton et al., 1999; Mills et al., 2003; Song & Katayama, 2005). It may be a normal process at contaminated sites, because of the changing degradation conditions (Kaplan & Kitts, 2004) and the obvious lack of one universal degradation specialist (Mueller et al., 1997) during the whole degradation process. Kaplan & Kitts (2004) have shown that the genera Alcaligenes, Microbacterium and Bacteroides as well as the genera Thermomonas and Rhodanobacter superseded the formerly abundant groups in a land treatment unit. The hybridisation experiments in the present study were not able to provide information about this aspect. This is admittedly a general problem in hybridisation experiments. You get only a signal for those microorganisms in which you are interested.
The diversity analysis of the active stage of bioremediation system Rositz 3 showed a predominance of Pseudomonas spp., which could not be confirmed by the hybridisation experiments. One reason therefore could be that the obtained clone distribution is not the same as the species distribution in an environmental sample (Fuhrmann et al., 1993). Especially the lack of the genus Pseudomonas in later samples was amazing, although this genus is known for its degradation potential (Evans et al., 2004; Mills et al., 2003). But disappearance of this genus has been also observed in other studies. Kaplan & Kitts (2004) detected an increase of pseudomonads in contaminated soil at the early stage of bioremediation, while other microorganisms dominated the community at the end of the process. A possible reason for community changes
Taxonomic aspects 19 is the observed two-phase pattern of degradation. The study of Heiss-Blanquet et al. (2005) reported that pseudomonads dominated nutrient-rich habitats and they decreased when conditions were less favourable.
Soil material from oil tank storage areas Grimma and Espenhain were investigated at the beginning of the technical bioremediation process. Except for Acinetobacter spp. and sequences similar to the clone TRS13 group all genera of interest were detected in these heap materials. The microbial community of both was dominated by pseudomonads and differed only slightly from the Rositz material. Despite of very different contamination levels membrane hybridisation results for these samples demonstrated only little differences in the frequency of detected bacteria groups. The microbial community in Grimma and Espenhain soil material was well detected with this small number of genera as mentioned above for the Rositz heaps at the beginning of the technical process. This low number of genera, which represented the main portion of the bacterioflora, has also been reported on other contaminated sites, where the diversity of a community decreased with a higher contamination level (Fahy et al., 2005; Saadoun, 2002).
Many of the detected microorganisms should be involved on the degradation of the pollutants in such bioremediation heaps. However, taxonomic analyses of microbial communities allow only some conclusions on the metabolic potential of the microorganisms or their functions in the environment (Dumont & Murrell, 2005) e.g. on the degradation potential for of mineral oil hydrocarbons. Therefore the focus has to be on physiological markers, e.g. catabolic genes, which are characteristic for the respective degradation process. 20 Physiological markers
1.4 Molecular methods for the detection of the degradation activity in a heap – Physiological markers
Mineral oil hydrocarbons consist mainly of linear alkanes, cyclic alkanes and aromatic compounds. The applied off-site remediation strategy exposes the soil to atmospheric oxygen, which results in direct evaporation of volatile compounds, like BTEX and short chain alkanes. The following ventilation of bioremediation heaps stimulates the aerobic degradation of mineral oil hydrocarbons compounds, a process which is well investigated (Smith, 1990; van Beilen et al., 2001; van Beilen et al., 2003). Therefore, the slow process of anaerobic degradation (Heider & Fuchs, 1997; Holliger & Zehnder, 1996; Widdel & Rabus, 2001) has no significant impact in such a system and is neglected in the further considerations.
The characteristic catabolic genes involved in the aerobic degradation of mineral oil hydrocarbons are the alkane hydroxylase (AlkB; EC 1.14.15.3) for alkane degradation (van Beilen et al., 2003) as well as the catechol 1,2- dioxygenase (C12O; EC 1.13.11.1) and the catechol 2,3-dioxygenase (C23O; EC 1.13.11.2) for degradation of aromatic compounds (Smith, 1990).
The aerobic degradation of (long chain) alkanes is initialised by alkane hydroxylase (Fig. 4). Alkanes are oxidised to the corresponding alcohol and further to fatty acids (van Beilen et al., 2003). All intermediates of this degradation pathway downstream of alcohol formation are also products of other degradation processes in soil. So the alkane hydroxylase was chosen as key catabolic enzyme for alkane degradation in this investigation, and the other enzyme classes involved in the oxidation of alkanes (van Beilen et al., 2003) are not considered here. Physiological markers 21
H3C CH2 CH2 CH3 n-alkane n
NADH O2 alkane hydroxylase + NAD H2O
H3C CH2 CH2 CH2OH alcohol n
ß-oxidation
acetyl-CoA
FIG. 4: Aerobic degradation pathway for alkanes including the key catabolic enzyme alkane hydroxylase (AlkB)
The aerobic degradation of diverse aromatic compounds is subdivided into an upper and a lower pathway (Fig. 5). There is a very limited number of central intermediates, which are subject to ring cleavage. Among them catechol and substituted catechols appear to be most important for the catabolism hazardous compounds. Catechol can be further degraded via ortho- or meta-cleavage (Harwood & Parales, 1996). The involved oxidative enzymes are catechol 1,2- dioxygenase and catechol 2,3-dioxygenase, respectively (Sei et al., 2004), the genes of which are used as key degradation genes for aromatic compounds here. Genes of the upper pathway appeared to be less useful, because they are specific for certain groups of compounds. For the other genes of the lower pathways the sequence database does not cover sequence diversity as well as for the oxygenases. 22 Physiological markers
CH2 CH3
CH3
Abbauweg upper OH degradation pathway OH lower catechol degradation pathway O O 2 catechol 1,2- catechol 2,3- 2 ortho-cleavage- dioxygenase dioxygenase meta-cleavage-
COOH CHO COOH
COOH OHH cis,cis - muconic acid 2-hydroxymuconic semialdehyd
SuccinatSuccinate + Pyruvate + Acetyl-CoA AcetaldehydeAcetaldehyd
FIG. 5: Aerobic degradation of aromatic compounds via the central intermediate catechol. Catechol is further degraded by ortho- or meta-cleavage, respectively.
Alkane hydroxylases have been studied with pure cultures as well as in environmental samples (Schwartz & McCoy, 1973; Smits et al., 1999; van Beilen et al., 2003). The best investigated alkane hydroxylase originates from strain Pseudomonas putida GPo1. It is an integral membrane non-heme iron oxygenase, whose gene is located on the large OCT plasmid (Smits et al., 2002; van Beilen et al., 2001). This plasmid contains alkB the alkane hydroxylase gene, and the other genes encoding alkane oxidation in two operons alkBFGHJKL and alkST (Janssen et al., 2005). Several studies have shown that alkane hydroxylase systems are subject to horizontal gene transfer, because they are widespread in bacterial genera.
Physiological markers 23
Of the catechol 2,3-dioxygenases the so-called subgroup I.2.A is especially well studied (Eltis & Bolin, 1996; Junca & Pieper, 2003; Mesarch et al., 2000; Meyer et al., 1999). These are two-domain iron containing-enzymes, which prefer monocyclic and bicyclic substrates (Eltis & Bolin, 1996). The first example of this type of catechol 2,3-dioxygenase (XylE) was from Pseudomonas putida mt-2 (Nakai et al., 1983), but numerous others have later been cloned and characterised from other bacteria (Kang et al., 1998; Moon et al., 1995). The catechol 1,2-dioxygenases has been also studied recently (Eulberg et al., 1997; Hou et al., 1977; Murakami et al., 1997; Sei et al., 1999).
It is known, that several strains have multiple alkane hydroxylases or catechol 2,3-dioxygenases (Iida et al., 2002; Smits et al., 2002; Whyte et al., 2002b). Possibly, this allows the strains to use a broad substrate spectrum as carbon source (Smits et al., 2002; Whyte et al., 2002b). It has been also described that the individual alkane hydroxylases in one strain may be expressed in different growth phases independent of substrate range (Marin et al., 2003).
Because of the widespread distribution of these catabolic genes in various genera and thus the high sequence diversities, the detection of all alkane hydroxylases (Vomberg & Klinner, 2000) and catechol dioxygenases with one primer pair can not be successful. Consequently, many primer pairs are described in the literature for the detection of genes for alkane hydroxylases (Ratajczak et al., 1998; Sei et al., 2003; Smits et al., 1999; van Beilen et al., 2001; Whyte et al., 2002b) and for catechol dioxygenases (Hallier-Soulier et al., 1996; Hendrickx et al., 2006; Meyer et al., 1999; Sei et al., 1999). Thus, it is important to choose suitable primer pairs for the respective application. However, sometimes it is necessary to design new primer pairs by means of the sequence database.
The following primer pairs for the catabolic genes detect most of the genera, which were identified as abundant members of the microbial community of a bioremediation heap. The primers developed by Smits et al. (1999) detect many alkB genes from different genera, mainly from Gammaproteobacteria, but also from Betaproteobacteria and Actinobacteria. In this way, the microbial diversity of the active material of bioremediation heap Rositz 3, excluding the 24 Physiological markers sphingomonads, was well covered. However, only little is known about alkane hydroxylases in Sphingomonas spp. (Vomberg & Klinner, 2000).
Meyer et al. (1999) designed a primer pair to detect the subgroup I.2.A, of catechol 2,3-dioxygenases, which especially covers the enzymes of pseudomonads and sphingomonads. These primers were suitable used for the detection of genes for C23O here, because the sequencing analysis showed that these genera were dominant in the material from active bioremediation heap Rositz 3. The detection of genes for C12O was tested with a new primer set available for gram positives (Thiel, unpub.).
The detection of the key catabolic genes, which are actively involved in the degradation process, should best be mRNA based. The extraction of mRNA from soil is indeed possible but tricky and not always reliable. Furthermore, the amount of extracted mRNA is often too low for quantitative investigations (Bürgmann et al., 2003). However, it is no problem to detect the existence of the degradation potential in soil on the DNA level, which could be shown in this study. To assess the degradation potential in soil material the detection of the availability of the catabolic genes is insufficient. Therefore quantitative statements are necessary, which were obtained in this study by competitive quantitative PCR with an internal standard (Wang & Mark, 1990). This method allowed the quantification of PCR products, e.g. gene fragments, which can be calculated as copy number per ng DNA extract.
The quantification of alkane hydroxylase and catechol 2,3-dioxygenase genes has been described in several publications (Heiss-Blanquet et al., 2005; Mesarch et al., 2000; Wikström et al., 1996). The analysis of alkane hydroxylase genes on DNA level by Heiss-Blanquet et al. (2005) is well comparable to the experiments performed in this study. The main focus of Heiss-Blanquet et al. (2005) was on the degradation of short-chain alkanes. They amplified alkB with different primer pairs to cover the high gene diversity. The genes for AlkB from gram positive strains, in particular from rhodococci dominated the investigated soils, while genes for AlkB from “Pseudomonas group 1” were much less abundant. It has also been reported, that there is no obvious difference in gene copy number of pristine and contaminated soils (Heiss-Blanquet et al., 2005), as the soil material was not involved in an active
Physiological markers 25 degradation process. Whyte et al. (2002a) used also different primer pairs to detect the diversity of alkB genes in Artic and Antartic hydrocarbon- contaminated and pristine soils. The authors reported that Pseudomonas spp. may be enriched in soil samples after the contamination event, whereas Rhodococcus spp. may be predominant in contaminated as well as in pristine soils. But these results have been not verified with quantitative analyses. In quantification experiments of Mesarch et al. (2000) and Wikström et al. (1996) for catechol 2,3-dioxygenases in soils no standard value has been defined for the C23O concentration. Thus a comparison with other soil materials is difficult. Mesarch et al. (2000) even seeded the soil with Pseudomonas sp. PpG7 cells and not only the indigenous microflora was detected.
The detection of the catabolic genes by PCR in this study was successful for AlkB and C23O but not for C12O. Detailed information about this aspect are given in the third part of this dissertation. The primers for C12O were designed, as mentioned above, for gram positive bacteria, which, however, were not dominant in the active heap Rositz 3. This might be one reason for the faileure of the PCR detection. Furthermore different copy numbers of catechol 1,2- dioxygenase and catechol 2,3-dioxygenase were also reported for contaminated sites (Sei et al., 2004). According to this paper, bacterial populations possessing C23O DNA seem to be dominant in samples from high- load conditions and from aged contaminations, like in the investigated samples here. So the detection of the degradation potential for aromatic compounds focused on the catechol 2,3-dioxygenase in the present study.
In general, more copies of genes for alkane hydroxylases than for catechol 2,3- dioxygenases were obtained in the soil samples of the present study. This correlates with the fact that alkanes are the dominant contamination in all investigated samples (Schmiers, 1973). Furthermore the used primers for alkB cover a broader range of microorganisms than the primers for C23O, which could be another explanation for the higher alkB copy numbers. The copy than number of genes for alkane hydroxylases in this study reached similar values as described for “Pseudomonas group 1” in the study of Heiss-Blanquet et al. (2005). Furthermore, in most samples in the present study more copies of degradation genes were detected in extracts from the first bead treatment than in extracts from the second/third bead treatment. This indicates that the main 26 Physiological markers portion of the degradation process was performed by bacteria located at the outside of soil particles. There, optimal oxygen supply for microorganisms is available, which is essential for aerobic degradation processes. The copy number of the catabolic genes in all Rositz heaps was nearly constant over the whole degradation process. A high concentration of the genes could be already detected at the beginning of the process. There was no clear correlation with the temperature gradient. The copy numbers remained constant on a high level, when the active genera from the beginning of the degradation process disappeared. So the copy number of catabolic genes may be more suitable for the description of the degradation process than taxonomic analyses.
The copy numbers of the genes of AlkB in soil material of the heaps Grimma and Espenhain were 2 – 6-fold smaller than in material of the Rositz heaps. Nearly the same level of copy numbers could be detected for genes of C23O in all investigated heaps. It was surprising that the copy numbers of the catabolic genes do not differ in Grimma and Espenhain soil material, although the concentration of mineral oil hydrocarbons in Espenhain soil material was 4-fold higher than in Grimma soil material. These data do not allow a conclusion on how many copy numbers are really necessary for a successful biodegradation. It may be essential to sample an uncontaminated reference site close to the contaminated site. This would allow to estimate weather an enrichment of degraders occurred due to the contamination. Such a strategy will not always be applicable for technical reasons, at least for smaller contaminated areas like filling-stations, however, this might be helpful.
Based on the obtained results it is not possible to say, which bacteria harbour the detected degradation genes. But a sequencing analysis of the gene fragments neither allows to answer this question because of the possible horizontal gene transfer of the gene clusters on plasmids (Smits et al., 2002; van Beilen et al., 2001).
A common problem in microbial ecology is the identification of those microorganisms, which carry out a specific set of metabolic processes in the environment. The formerly applied culture-dependent methods resolve this problem, but, as mentioned above, they do not reflect the reality in the soil (Dumont & Murrell, 2005; Felske et al., 1999; Torsvik et al., 1990). In recent years some methods have been developed that combine culture-independent
Physiological markers 27 identification of microorganisms with metabolic analyses e.g. FISH- microautoradiography (FISH-MAR) (Lee et al., 1999), isotope array (Adamczyk et al., 2003), using radioactive tracers and stable isotope probing (SIP) (Dumont & Murrell, 2005; Manefield et al., 2002), using stable isotope-enriched substrates which at the moment is mainly 13C.
It has been reported that DNA-based stable isotope probing (SIP) was successfully used to study the functionally active populations of methanotrophs in peat soil (Morris et al., 2002) and acidic forest soil (Radajewski et al., 2002). Labelled multi-carbon compounds were used for the detection of the biodegradation of organic pollutants as well (Padmanabhan et al., 2003). Also RNA-based SIP has been used for the identification of phenol degraders in an aerobic industry reactor and of pentachlorophenol degraders in soil samples (Dumont & Murrell, 2005). Now the question is, does this method really allow to detect the active degrading microorganisms in a soil of an aged mineral oil hydrocarbon contamination? Or do other bacteria of the microbial community use the added stable isotope-enriched substrates in the soil? This should be the focus in future research in the field of who is doing what in contaminated sites.
28 Concluding remarks
1.5 Concluding remarks
In the present study, the community structure and the degradation potential in bioremediation systems for the treatment of contaminated soil were investigated with molecular methods. In contrast to sum parameters, as soil respiration or temperature measurement, molecular methods should help to resolve the problem that microbiology in bioremediation systems is still considered as a black box.
Microbial degradation processes are used for the cleaning of contaminated soils, but are influenced by many parameters. It is not always known which factor is responsible for an insufficient remediation. Molecular tools may be a further component to unravel this question.
To my knowledge this was the first analysis of the active members of the microbial community in a bioremediation heap were performed on SSU rRNA. Recent studies used mainly culture-dependent methods or culture-independent methods on SSU rDNA level for community analyses. Both obtain insufficient results for the detection of active bacteria in an environment.
The taxonomic analysis in the persent study showed that Gammaproteobacteria especially pseudomonads dominated the clone library. The occurrence of Pseudomonas spp. was not surprising, because they are known for their fast growth under high substrate concentrations. The sequences were indeed similar to the common genus Pseudomonas, but often formed subgroups, for which no cultivated representatives are available. Furthermore sphingomonads (Alphaproteobacteria) were dominant, which are also known for their broad degradation potential. Also Betaproteobacteria of the genera Acidovorax, Rhodoferax and Thiobacillus may play a decisive role in such a soil heap, which are common members in aquatic environments. A new insight was that Thiobacillus spp. occurred in mineral oil hydrocarbon contaminated sites, because they are known mainly for autotrophic oxidation of sulphur compounds. Gram positive bacteria were detected only with a small portion.
An interesting methodological aspect for the taxonomic analysis was the multiple bead treatment of the soil for nucleic acid extraction. The differences of the sequence results between the first and the second/third bead treatment were obviously not on the phylum level. Some genera were only detectable in Concluding remarks 29 the first extract, e.g. Sphingomonas spp. and some only in the second/third extract, e.g. Zymomonas spp. and Acinetobacter spp.
The different frequencies of sequences in a clone library show a trend in the species distribution in the environmental sample. However, because of PCR and cloning biases a quantitative analysis is relatively difficult. Therefore hybridisation experiments are necessary. But this method has also a disadvantage, as only a signal for those genera is obtained in which one is interested. Probes on genus level were applied in this study. The selection of the probes based on the previous taxonomic analysis. The results of the membrane hybridisation showed that the dominant genera in the clone library may play an decisive role at the beginning und during the first active degradation phase. Afterwards enormous changes of the community were observed, thus the detected genera contributed only a small portion to the bacterioflora. This was not expected to such an extent.
Furthermore the quantification of the catabolic marker genes for degradation of mineral oil hydrocarbons was performed by quantitative competitive PCR in this study. However, the quantification was done on DNA level, as the extraction of mRNA from soil samples is tricky and quantitative conclusion not always possible. Genes for alkane hydroxylase (AlkB) indicating the alkane oxidation potential were more abundant in the heap material than genes for catechol 2,3- dioxygenase (C23O) indicating the potential for oxidation of aromatic compounds. The copy number of genes were nearly at the same level during the technical remediation process. Astonishing was that the community changes were not reflected in the copy numbers of the catabolic genes. So the catabolic genes may better detect the degradation potential in a bioremediation heap than the taxonomic probes.
All these methods have only an insufficient ability to identify those microorganisms which are actually involved in the degradation process. In the last years some techniques, as stable isotope probing, were developed that combine cultivation-independent methods for the identification of microorganisms with metabolic analyses. The future application of such techniques could give a further insight in the bioremediation process of such heaps.
2
Microbial diversity in the active stage of a bioremediation system for mineral oil hydrocarbon contaminated soils
Nicole Popp1, Michael Schlömann1 and Margit Mau1
1 Interdisziplinäres Ökologisches Zentrum, TU Bergakademie Freiberg, Leipziger Str. 29, D-09599 Freiberg, Germany
Running title: Microbial diversity in a bioremediation system
Correspondence: Michael Schlömann [email protected] 32 Microbial diversity in a bioremediation system
ABSTRACT
Soils contaminated with mineral oil hydrocarbons are often cleaned in off-site bioremediation systems. Overall degradation activity of the indigenous microflora in such plants may be followed by recording temperature or soil respiration. In order to find out which bacteria are responsible for the degradation, the diversity of the active microflora in a degrading soil remediation system was investigated by SSU rRNA analysis. Two sequential RNA extracts were generated by a procedure comprising bead beating, the first extract containing RNA predominating those of microorganisms, which were presumably easy to lyse or located outside of soil particles, and the second extract with RNA of microorganisms, which were possibly harder to lyse or located inside of soil particles. Both extracts were analysed separately by generating individual SSU rDNA clone libraries from cDNA of the two extracts. The sequencing results showed moderate diversity. The two clone libraries were dominated by Gammaproteobacteria, especially Pseudomonas spp. Alphaproteobacteria and Betaproteobacteria comprised two other large groups in the clone libraries. Actinobacteria, Firmicutes, Bacteroidetes and Epsilonproteobacteria were detected in lower numbers. The obtained sequences predominantly were related to genera, for which cultivated representatives have been described, but often clustered together in the phylogenetic tree and the most similar sequences originated from soils and not from pure cultures. Some of the most similar sequences were described in investigations on mineral oil degradation others were obtained in studies where the bacteria were reported to grow under microaerobic conditions. Some genera, like sphingomonads or Acinetobacter spp. were only detectable in one of the two sequential RNA extracts, others like Acidovorax spp. were detected in both extracts. In the latter case, sequences from the sequential extracts formed separate clusters in the phylogenetic tree.
INTRODUCTION
The contamination of soils with mineral oil hydrocarbons is a widespread environmental problem. Mineral oil hydrocarbons are not really xenobiotic, but their large-scale use and various applications in many cases lead to
Microbial diversity in a bioremediation system 33 environmental contaminations (Gallego et al., 2001). The reasons for such contaminations may be found in petroleum transport, storage and refining or accidental events (Juteau et al., 2003). Soil is an environmental compartment, which is renewable only within long time periods or even not renewable at all. Therefore, it is important to protect soil from such contaminations or to clean contaminated soils. After treatment, the soil should be usable, at least in areas with low purity requirement like road construction or recultivation of industrial areas. One possibility for cleaning such soils is bioremediation (Juteau et al., 2003), which aims at biological mineralisation of organic compounds to CO2 and water or at least at transformation to less toxic or innocuous forms (Plaza et al., 2003). The success of biodegradation depends on the predominant environmental conditions, on the chemical structure of the pollutants, on the bioavailability of the contaminating compounds, and thus on the interaction between pollutant, soil matrix and microorganisms (Del'Arco & de França, 2001; Volkering et al., 1998). Another problem of contaminated sites is the wide variety of mineral oil hydrocarbons (Li et al., 2004). There are many known consortia of microorganisms, which are able to degrade mineral oil hydrocarbons under laboratory or field conditions (Ratajczak et al., 1998; Wikström et al., 1996). However, a single bacterium, usually, has only a relatively small degradation range and thus, not all fractions of the mineral oil hydrocarbons can be degraded by a single species (Mishra et al., 2001). Consequently, the use of a laboratory strain for cleaning a contaminated soil is usually not successful. In addition to the restricted substrate range, such a strain is not necessarily adapted to the respective environmental conditions, like temperature, water availability, O2 and nutrient supplies. Rarely, contaminations occur suddenly (e.g. by tanker accidents or explosions), but more often the contamination is creeping. A tank of a petrol station or a pipeline may leak for many years and contaminate the surrounding soil, and thus a degrading indigenous microflora can develop and adapt to the use of pollutants as C- and energy source. For that reason, the naturally occurring microflora is often used for the degradation of mineral oil hydrocarbons (Watanabe, 2001).
34 Microbial diversity in a bioremediation system
In practice, a variety of methods is used for the bioremediation of hydrocarbon contaminated soils with off-site remediation being the most important remediation strategy. Often the soil is ventilated and a mineral fertilizer may be added at the beginning of or during the treatment. During the bioremediation process in such systems the temperature of the soil may increase (Condé & Hagedorn, 1997), which reflects the activity of the indigenous microflora using the mineral oil hydrocarbons as C- and energy source. For better control of such a bioremediation system, it may be helpful to know which microorganisms actually bring about the biodegradation. Various methods have been used to analyse the microbial diversity in mineral oil hydrocarbon-contaminated soils. One method is the cultivation of bacteria (Chao & Hsu, 2004; Greene et al., 2000; La Rosa et al., 2006; Plaza et al., 2003; Radwan et al., 1995; Saul et al., 2005), but it is assumed that only circa one percent of the soil microorganisms can be cultivated with usual methods (Torsvik et al., 1990). The established cultivation methods favour the isolation of fast growing microorganisms and of those accepting high nutrient concentrations (Felske et al., 1999). Thus, for diversity analyses, cultivation- independent methods are more reliable. Some studies described the mineral oil hydrocarbon-degrading community on SSU rDNA level in soils (Juck et al., 2000; Saul et al., 2005), in a biodegradation system (Kaplan & Kitts, 2004), in coastal material (Macnaughton et al., 1999) or in soils treated in bioreactors (Mills et al., 2003). SSU rDNA reflects both active and inactive microorganisms and thus more the genetic potential rather than the active portion of a microbial community (Lüdemann et al., 2000). The active community in a bioremediation system may be characterised by investigation of SSU rRNA, because the latter is directly involved in active protein synthesis (Morgan et al., 2002). In addition, after release from lysed bacterial cells into the soil, the SSU rRNA is chemically degraded relatively fast and thus does not remain intact in the soil, while SSU rDNA is more stable (Trevors, 1996a). There is an investigation about metabolically active members of a bacterial community in a polychlorinated biphenyl-polluted soil with no degrading activity (Nogales et al., 1999). But, to our knowledge SSU rRNA analyses of mineral oil hydrocarbon-degrading communities have so far not been described in the literature. Active microorganisms should especially be present in phases of temperature
Microbial diversity in a bioremediation system 35 increase. Thus, in the present study samples from the “hot” stage of a bioremediation system were investigated by SSU rRNA analysis to show, which microorganisms are responsible for degradation.
MATERIALS AND METHODS
Soil samples Soil samples were obtained from a bioremediation system for material from the contaminated site Rositz. Rositz is a village in Thuringia (Germany), where a tar factory was processing brown coal tar between 1917 and 1990. The factory was destroyed in World War II, which is still the main reason for the contaminated soil in this area. The contamination consists of alkanes, BTEX- and polyaromatic compounds as well as phenols (Verwaltungs- und Verwertungsgesellschaft Industriegelände Rositz mbH, 1998), which remained on-site for more than 60 years. The soil had a silty and a partially loamy structure. The bioremediation heap Rositz 3 was composed of 700 t mineral oil contaminated soil material (8000 mg mineral oil per kg soil dry weight), with a basis of 60 m² and 3.5 m height. The bioremediation heap Rositz 3 was permanently ventilated (Condé & Hagedorn, 1997) and 0,5 t mineral fertiliser (NPK 15:15:15) had initially been added to 1000 t soil. Soil samples in which according to chemical analyses the concentration of mineral oil hydrocarbons had dropped to < 6000 mg per kg soil dry weight and in which the temperature inside the heap had increased from 12 °C to 21 °C within 43 days were considered to contain an actively degrading microflora and were used for the analysis of microbial diversity. The sampling of the active bioremediation system Rositz 3 took place on April 24th 2002. The temperature inside the heap (21 °C) was 10 °C above the surrounding temperature on this day. The sample was collected at a central point with a special drill for manual soil sampling from 1.5 m height and 0.75 m into the system and the soil material was put into a 50 ml reaction tube and was frozen at –80 °C.
Nucleic acid extraction For the simultaneous extraction of RNA and DNA from the soil sample the method of Görres (2001) was used in a modified form. The applied nucleic acid
36 Microbial diversity in a bioremediation system extraction method should allow the separation of the nucleic acid extract into sequential fractions and thus provides the possibility to perform a differential analysis. The first extract may contain the nucleic acids of microorganisms which are easy to lyse or located outside of soil particles. The following extracts might contain nucleic acids of the less accessible organisms. Soil (0.5 – 1 g) was transferred into a 2 ml reaction tube with 0.5 g glass beads of 0.1 – 0.25 mm diameter (Roth). 750 µl of 120 mM sodium phosphate buffer (pH 8) and 500 µl phenol:chloroform:isoamyl alcohol (25:24:1 v/v/v) were added. The tubes were placed into a bead beater (MM200, Retsch) for 2 min at 26.6 rounds s-1. To separate the aqueous and the organic phases the tubes were centrifuged at 12000 g for 5 min. The aqueous layers were transferred to a new 2 ml reaction tube and stored on ice. This tube contained the first nucleic acid extract. The brown phenol:chloroform layer was removed from the tube with the soil-beads mixture and discarded. 500 µl of the sodium phosphate buffer and 500 µl fresh phenol:chloroform:isoamyl alcohol were added to the soil bead mixture and the bead-beating procedure was repeated. The aqueous phases were transferred into a new 2 ml reaction tube and stored on ice (second extract). The extraction step was repeated once again and the aqueous phase of this step was combined with the aqueous phase from the second extraction. Further treatment of this second/third extract was separate from the first nucleic acid extract. The preparations were extracted three times with 1 ml phenol:chloroform:isoamyl alcohol. Nucleic acids were precipitated by adding solutions of 5 M NaCl and 30 % (w/v) PEG 6000 at the ratio of sample:salt:PEG 6000 of 9:1:10 (v/v/v). The mixtures were left at room temperature for 2 h. To pellet the nucleic acids the tubes were centrifuged at 12000 g for 5 min and the resulting supernatants were discarded. The nucleic acid pellets were washed with 500 µl of 80 % (v/v) ethanol, centrifuged at 12000 g for 5 min and the ethanol was removed. This step was repeated once. The pellets were dried at room temperature for about 30 min and the nucleic acids were resuspended in 50 µl deionised water and stored at –20 °C. The nucleic acids were visualised by an urea denaturing 4.5 % PAA gel and ethidium bromide staining. Bands of DNA as well as SSU rRNA and LSU rRNA were visible.
Microbial diversity in a bioremediation system 37
Separation of RNA and DNA Separation of RNA and DNA was performed with the nucleic acid binding silica spin-columns of an Invisorb TwinSpin Cell Mini Kit (Invitek) using the buffers supplied by the manufacturers. The manufacturer’s protocol was modified on some points: Lysis Solution T which is meant to lyse cells, was added to the nucleic acid extracts from soil to obtain the optimal reaction conditions for the spin columns. At the end, the elutions of the RNA and the DNA from the respective binding-spin filters were carried out twice. The DNA extracts were stored at +4 °C and were later used for another application. To further purify the RNA extracts, traces of DNA were digested with 60 U of RNase-free DNaseI (Roche) in the enzyme-specific buffer (10 mM Tris-HCl (pH
7.5); 2.5 mM MgCl2; 0.1 mM CaCl2) (Ambion) at 37 °C for 2 h. DNaseI was removed by phenol extraction and ethanol precipitation (Moore, 1987). The RNA extracts were stored at –20 °C. The success of the DNaseI digestion was checked with a SSU rDNA PCR reaction, of which no PCR product could be visualised by 1 % agarose gel electrophoresis.
Amplification of SSU rRNA by reverse transcription (rT)-PCR The SSU rRNA was used as template for amplification of corresponding cDNA by rT-PCR using a TGradient Thermocycler (Whatman Biometra). The primers 27F, 5’-AGAGTTTGATCCTGGCTCAG-3’, and 519R, 5’- G(TA)ATTACCGCGGC(GT)GCTG-3’ (obtained from MWG Biotech) target most bacterial SSU ribosomal RNAs (Lane, 1991). The rT-PCR reactions were prepared following the instruction of the GeneAmp EZ rTth RNA PCR Kit (Perkin Elmer-Applied Biosystems). The 50 µl reaction mixture contained the kit components and final concentrations of 0.25 µM of each primer, 0.1 µg BSA µl-1 (MBI Fermentas) and 1 µl RNA extract. The reactions were prepared without RNA extract for negative controls. The rTth DNA Polymerase, which is able to perform the reverse transcription step and the amplification step (PCR), was added to the reactions after 1 min at 60 °C. The following PCR programme was used: 30 min at 60 °C (reverse transcription); initial denaturation for 2 min at 94 °C, 40 cycles (1 min at 94 °C, 30 s at 55 °C, 45 s at 72 °C), terminal elongation for 7 min at 72 °C, indefinitely at 15 °C (modified from the instruction of GeneAmp EZ rTth RNA PCR Kit).
38 Microbial diversity in a bioremediation system
The rT-PCR products were visualised by 1 % agarose gel electrophoresis and ethidium bromide staining. Bands of rT-PCR products were excised from the agarose gels and DNA was isolated with an EasyPure DNA Purification Kit (Biozym).
Cloning and screening of rT-PCR products RT-PCR products were ligated into a T-vector (Marchuk et al., 1991), which was prepared from EcoRV-digested pBluescript II SK(+) (Stratagene). The transformation into competent Escherichia coli DH5α (Gibco-BRL) was performed by electroporation using an EquiBio device (peqlab Biotechnologie GmbH) and the standard protocol for transformation of E. coli described in the optimisation guide of EquiBio. The cell suspensions were plated onto Luria- Bertani (LB) medium (1 % tryptone, 0.5 % yeast extract, 1 % NaCl, 1.5 % agar) with 50 µg ampicillin ml-1 and the clones were screened by blue white detection (Sambrook & Russell, 2001). White clones were screened for inserts by large-scale colony screening (Campbell & Choy, 2001). Extraction of the plasmid DNA from liquid cultures was performed using the FlexiPrep Kit (Amersham Biosciences) and the bound plasmid DNA was eluted with deionised water and stored at –20 °C. Clones from the first bead treatment were named Rositz1 Roxx and clones from the second/third bead treatment were named Rositz23 Rosxx, were xx indicates the clone number in the respective library. The final test for inserts of expected size was by PvuII digestion of the plasmid DNA.
DNA sequencing and phylogenetic analysis Sequencing was done on a LI-COR 4200 IR Sequencer using the CycleReader Auto DNA Sequencing Kit (MBI Fermentas) with labelled primer T3 (5‘-CGCGCAATTAACCCTCACTAAAG-3‘) (Stratagene). The following program was performed: initial denaturation 2 min at 92 °C, 25 cycles (30 s at 92 °C, 30 s at 55 °C and 1 min at 72 °C). Sequence analysis was performed using the programs e-Seq (LI-COR) and EditSeq (DNASTAR, Lasergene). Using BLAST (Altschul et al., 1990) the most similar SSU rRNA sequences were identified. Using the program tool Chimera
Microbial diversity in a bioremediation system 39
Check of Ribosomal Database Project II, the obtained sequences were tested for potential chimeric sequences (Cole et al., 2003). The ARB software was applied for the phylogenetic analysis of sequences (Ludwig et al., 2004). Initial alignment was done by the ARB fast-aligner and then manual verifications and corrections were necessary. At first, a main tree including reference sequences of the taxonomic group of interest was calculated with the maximum likelihood method and the respective taxonomic filter, which included only positions, which are conserved among at least 50 % of the sequences in the respective taxonomic group. After this, the Rositz sequences were added to the tree with the parsimony method applying the respective filter. So the tree topology does not change. Finally, an outgroup was defined and added to the tree with the parsimony method by applying the universal bacterial filter. In some cases, many sequences were very similar to another, and were therefore grouped together. Such a group was named with the representative species name and the numbers of corresponding Rositz sequences were specified in brackets.
RESULTS AND DISCUSSION
Construction of SSU rRNA library The aim of this study was the characterisation of the active microbial community in the bioremediation system Rositz 3 degrading mineral oil hydrocarbons. Therefore, a sample was taken during the active period of the heap and was subjected to rRNA-based sequencing analysis. The amplification of bacterial SSU rRNA was performed for the extract of the first bead treatment and separately for the extract of the second/third bead treatment to investigate the influence of the sequential extraction. In total 224 clones were analysed, 112 from the first and 112 from the second/third bead treatment. All clones were sequenced over their full lengths. Eight sequences were assumed to be chimeras according to results of the Chimera check program (Cole et al., 2003). These sequences were split into two parts at the specified position, and the two parts were handled separately for the next steps of the analysis.
40 Microbial diversity in a bioremediation system
Phylogenetic distribution of clones on the phylum and subphylum level: Predominance of Gammaproteobacteria For a first phylogenetic assignment, sequences were compared to the database with the BLAST program which showed the following phylogenetic distribution (Table 1): Proteobacteria dominated in both clone libraries. Gammaproteobacteria represented the main group. Alphaproteobacteria and Betaproteobacteria occurred with about the same frequency in the clone libraries, but made up only one third of the numbers of Gammaproteobacteria. Epsilonproteobacteria, Bacteroidetes, Actinobacteria and Firmicutes were detected in lower levels. Such less common sequences occurred at a somewhat higher level in the extract from the second/third bead treatment.
TABLE. 1: Phylogenetic distribution of SSU rRNA sequences recovered from two sequential extractions of the Rositz samples Absolute number of Relative number of sequen- Phylogenetic group sequences in extracts from ces (%) in extracts from 1 st bead 2 nd/3 rd bead 1 st bead 2 nd/3 rd bead treatment treatment treatment treatment Alphaproteobacteria 16 21 14 19 Betaproteobacteria 23 23 20 21 Gammaproteobacteria 64 54 57 48 Epsilonproteobacteria 4 1 4 1 Actinobacteria 4 6 4 5 Bacteroidetes 1 3 1 3 Firmicutes - 1 - 1
SSU rRNA sequencing reflected considerable diversity of the microflora, in which, however, Gammaproteobacteria clearly dominated. A similar phenomenon is known from cultivation-based analyses of environmental samples and has been termed “γ-shift”. It occurs under conditions with nutrient oversupply (Amann et al., 1995). Given that in pristine soils Bacillus spp. (Hugenholtz et al., 1998), Bacteroidetes as well as gram positives with low GC- content (Borneman et al., 1996) and Alphaproteobacteria (McCaig et al., 1999) are abundant, the dominance of Gammaproteobacteria may result from the degradation of high levels of contaminants. A “γ-shift” has also been described to occur upon contamination of soil (Chao & Hsu, 2004) and of Arctic sea ice (Gerdes et al., 2005). In the present case (Rositz), the contamination with
Microbial diversity in a bioremediation system 41
8000 mg mineral oil hydrocarbons per kg soil dry weight provided a high nutrient supply for degraders. This might have caused a γ-shift, although the ability to degrade mineral oil hydrocarbons is not at all limited to Gammaproteobacteria (Mills et al., 2003; Radwan et al., 1995; Vomberg & Klinner, 2000). The dominating group among the Gammaproteobacteria in this class was the genus Pseudomonas (Fig. 1a, Fig. 1b), but there was no clear association to one Pseudomonas species. The Rositz sequences were rather fairly distributed within the genus. One of the largest groups was the Pseudomonas anguilliseptica group within the P. aeruginosa lineage. Many of the other Rositz sequences clustered in the Pseudomonas syringae, the Pseudomonas fluorescens and the Pseudomonas putida lineage, respectively. Pseudomonas isolates from pinyon-juniper woodland soils were reported to form also such a P. syringae and a P. fluorescens lineage (Kuske et al., 1999), but no members of the P. aeruginosa and P. putida lineage were obtained in this study. The Rositz sequences within the P. anguilliseptica group originated from the extract after the first bead treatment, while members of the P. syringae and the P. fluorescens lineage were typical for the second/third bead treatment (see origin of the sequences in legend of Fig. 1b). An isolate similar to the Rositz sequences of the P. putida group was obtained in a study about the microflora in biomass-recycle reactors (Morgan et al., 2002). Due to work with isolated strains pseudomonads are often considered as the model organisms for pollutant degradation. And in fact several studies on contaminated environments reported on a dominance of pseudomonads similar findings. They were prevalent in mineral oil hydrocarbon contaminated soil under bioremediation (Evans et al., 2004; Mills et al., 2003), but also in pristine rhizosphere soil (Chow et al., 2002; Evans et al., 2004; Marilley & Aragno, 1999). Pseudomonas spp. increased transiently in the early phase of a bioremediation test on soil with mineral oil contamination (Kaplan & Kitts, 2004). This implied that pseudomonads degrade mainly those pollutants, which are well bioavailable (Heiss-Blanquet et al., 2005). At the end of this process, when most of the contamination was gone, other microorganisms were most prevalent (Kaplan & Kitts, 2004). Preliminary quantitative data on Rositz 3
42 Microbial diversity in a bioremediation system provide a similar impression on the prevalence of pseudomonads over the time course of treatment (data not shown).
FIG. 1a: Maximum likelihood phylogenetic tree of Gammaproteobacteria sequences from the active stage of the Rositz samples with Betaproteobacteria sequences as outgroup. The individual groups include the following sequences: Acinetobacter group: Rositz1: Ro7, Ro84; Rositz23: Ros24, Ros37, Ros76a, Ros89, Ros98; Group clone TRS13: uncultured eubacterium TRS13 (plant rhizosphere; AJ005875); Rositz1: Ro4a¸ Ro9, Ro55, Ro103, Ro120, Ro124, Ro133, Ro138; Rositz23: Ros13, Ros40, Ros119; Pseudomonas group is illustrated in Fig. 1b.
Microbial diversity in a bioremediation system 43
FIG. 1b: Maximum likelihood phylogenetic tree of the genus Pseudomonas sequences from the active stage of the Rositz samples with Alcanivorax borkumensis sequence as outgroup. The phylogenetic tree was calculated without a filter. The individual groups include the following sequences: P. anguilliseptica group: P. anguilliseptica (DSM12111; T); Rositz1: Ro8a, Ro18, Ro19, Ro23, Ro33, Ro39, Ro41, Ro45, Ro50, Ro58, Ro69, Ro72, Ro76, Ro85, Ro87, Ro88, Ro101, Ro102, Ro108, Ro112, Ro115, Ro128, Ro136; Rositz23: Ros19, Ros22, Ros28, Ros48, Ros50, Ros54, Ros68, Ros104, Ros105, Ros110, Ros113, Ros116; P. putida group: Rositz1: Ro47, Ro62, Ro104, Ro119b, Ro132; Rositz23: Ros63, Ros64, Ros65; P. syringae group: Rositz1: Ro14, Ro53, Ro56, Ro71, Ro90; Rositz23: Ros1, Ros15, Ros52, Ros60, Ros67, Ros75b, Ros81, Ros87, Ros94. * The classification of the Pseudomonas lineages followed that proposed by Kuske et al. (1999).
44 Microbial diversity in a bioremediation system
Another larger group of Rositz sequences within the Gammaproteobacteria showed high similarity to Acinetobacter spp. (Fig. 1a). It was recognizable on basis of the phylogenetic distribution that the Rositz sequences formed two clusters within the Moraxellaceae. A cluster of 7 Rositz sequences of the Acinetobacter group contained mostly sequences from the extract after the second/third bead treatment and most of the other Moraxellaceae-like Rositz sequences were from the first extract. High prevalence of Acinetobacter was reported mostly in connection with diesel contaminations. Thus, Acinetobacter concentration within a few days after contamination with diesel was reported to increase in soil (Chao & Hsu, 2004). Acinetobacter were also the most abundant diesel degrading isolates during a bioremediation test with diesel contaminated sandy soil (Gallego et al., 2001), which was biostimulated with nutrients and periodically tilled for aeration. Also Alkanindiges illinoisensis isolated from oilfield soils is able to degrade linear alkane hydrocarbons with chain-length below C16 (Bogan et al., 2003) Eight of the eleven Rositz sequences, which belonged to the clone TRS13 group, were obtained in the first bead treatment (Fig. 1a). Clone TRS13 was the closest relative of Rositz sequences and was found in an investigation on the phylogenetic diversity of bacterial communities in rhizosphere soil of Lolium perenne and Trifolium repens (Marilley & Aragno, 1999). Some individual sequences of the Rositz sample affiliated with the Xanthomonadaceae (Fig. 1a). The genus Xanthomonas was detected in petroleum hydrocarbon-contaminated soils (Juck et al., 2000). It was also reported that soil isolates related to Xanthomonas spp. and Stenotrophomonas spp. were able to utilize alkanes (Tesar et al., 2002). Hydrocarboniphaga effusa, a alkane degrader and a novel member of the Gammaproteobacteria was isolated from soil contaminated with fuel oil (Palleroni et al., 2004).
Diversity beyond Gammaproteobacteria: Known degraders and unexpected genera Clones of Alphaproteobacteria: Most clones related to Alphaproteobacteria were similar to Sphingomonas spp. (Fig. 2) and most of these sequences were obtained from the first bead treatment. The phylogenetic distribution demonstrated two main groups within the Sphingomonadaceae. One of the
Microbial diversity in a bioremediation system 45 clusters affiliated to the Sphingobium lineage and the second to Zymomonas mobilis ssp. mobilis. The Sphingobium lineage harbours also the strain Sphingomonas sp. DhA-95, which were isolated from a hydrocarbon- contaminated arctic soil and have the capability to degrade alkanes and jet-fuel (Yu et al., 2000).
FIG. 2: Maximum likelihood phylogenetic tree of Alphaproteobacteria sequences from the active stage of the Rositz samples with Betaproteobacteria sequences as outgroup. The individual groups include the following sequences: Sphingomonas group: Sphingomonas sp. DhA-95 (hydrocarbon-contaminated soil; AF177917) Rositz1: Ro8b, Ro25, Ro46, Ro48, Ro61, Ro81, Ro116, Ro123; Rositz23: Ros35, Ros79, Ros85, Ros86; Zymomonas group: Sphingomonas sp. B18 (freshwater; AF410927) Rositz1: Ro31, Ro110; Rositz23: Ros20; Ros57, Ros77, Ros92, Ros93, Ros108.
Sphingomonads are characteristic for a variety of subsurface environments and they are known for there broad degradation potential (Thiel et al., 2005; Vomberg & Klinner, 2000). Consequently, such microorganisms might have played a significant role in the bioremediation of the investigated soil. Similar to the present analysis, sphingomonads represented a dominant group besides the pseudomonads in a bioreactor treating petroleum-contaminated soil (Mills et al., 2003). However, Sphingomonas prevalence was low in other bioremediation systems that were investigated with culture-independent methods (Kaplan &
46 Microbial diversity in a bioremediation system
Kitts, 2004) or sphingomonads were not included in corresponding hybridisation analyses (Chao & Hsu, 2004). Six of the eight Rositz sequences, which belonged to the Zymomonas group, were obtained in the second/third bead treatment. Such microorganisms were typically isolated from ethanol fermenting samples (Dien et al., 2003) and not in degrading soil samples. Rositz sequences showed rather similarities to members of aquatic microflora, like Sphingomonas sp. B18 (O'Sullivan et al., 2002; Urbach et al., 2001; Wolf et al., 2003). The other Rositz sequences of this class showed a high similarity to the methylotrophic Methylobacterium spp. and to Rhodobacter sphaeroides, respectively, a facultative photoheterotrophic bacterium (Dryden & Kaplan, 1990). Sequences related to the Rhodobacteriaceae group were obtained in an oil-enriched microcosm (Yakimov et al., 2005). Also the genus Methylobacterium were detected in petroleum hydrocarbon-contaminated soils (Juck et al., 2000).
Betaproteobacteria: The Rositz clones showed a wide distribution in the phylogenetic tree of Betaproteobacteria (Fig. 3). However, three larger groups with 8 to 12 sequences each were recognizable. The sequences of interest affiliated to the genera Rhodoferax, Acidovorax, and Thiobacillus, respectively. The three larger sequence groups demonstrated an even distribution among the first and second/third bead treatment. The sequences of the Rositz Rhodoferax group were related to sequences obtained from an aquatic system (clone GKS2-77) (Glöckner et al., 2000) and from flooded paddy soil cores (clone oxSCC-37) (Lüdemann et al., 2000). No degradation potential for compounds of mineral oil hydrocarbons was described for the phototrophic Rhodoferax spp. Acidovorax-related Rositz sequences were quite common. Genus Acidovorax predominated enrichments from northern soils with polycyclic aromatic hydrocarbons as carbon source (Eriksson et al., 2003). However, similar sequences to the Rositz clones were found in an aquatic habitat (Huber et al., 2003). Another cluster of sequences was similar to those from Thiobacillus, a genus, which is known for autotrophic oxidation of sulphur compounds, although some species could also grow heterotrophicly (Kelly et al., 2005). A similar sequence (clone RB7C6) was also found in monochlorobenzene contaminated groundwater (Alfreider et al., 2002).
Microbial diversity in a bioremediation system 47
Some more sequences could be affiliated to an uncultured bacterium (clone 36- 9), which dominated the clone library from an aquifer contaminated with coal tar waste (Bakermans & Madsen, 2002), a contamination similar to Rositz. This cluster consisted exclusively of Rositz sequences from the first bead treatment. The Rositz sequences of Betaproteobacteria showed a striking relationship to sequences or strains from contaminated or pristine aquatic ecosystems, in which members of this class dominated.
FIG. 3: Maximum likelihood phylogenetic tree of Betaproteobacteria sequences from the active stage of the Rositz samples with Gammaproteobacteria sequences as outgroup. The individual groups include the following sequences: Rhodoferax group: uncultured bacterium GKS2-77 (freshwater; AJ290040); uncultured bacterium oxSCC-37 (paddy soil; AJ387875) Rositz1: Ro29, Ro34, Ro68, Ro80, Ro127; Rositz23: Ros26, Ros34, Ros61, Ros62, Ros72, Ros74, Ros118, Thiobacillus group: uncultured bacterium RB7C6 (groundwater treating reactor; AF407385); Rositz1: Ro3, Ro11, Ro111, Ro121; Rositz23: Ros12, Ros23, Ros69, Ros106
48 Microbial diversity in a bioremediation system
Epsilonproteobacteria: The four Rositz sequences from members of Epsilonproteobacteria (Fig. 4) were related to sequences from oil or chlorobenzene-contaminated groundwater (Alfreider et al., 2002; Kodama & Watanabe, 2003; Watanabe et al., 2000; Watanabe et al., 2002). The closest cultured relative Sulfuricurvum kujiense (ATCC BAA-921 (T)) was isolated from oil-contaminated groundwater. It was reported to be able to oxidise sulfur compounds in crude oil and to use the oxidation as energy source under microaerobic or anaerobic conditions (Kodama & Watanabe, 2003).
FIG. 4: Maximum likelihood phylogenetic tree of Epsilonproteobacteria sequences from the active stage of the Rositz samples. Sequence of Holophaga foetida serve as the outgroup.
Clones of Actinobacteria: The Actinobacteria-related Rositz sequences were phylogenetically very diverse (Fig. 5). Actinobacteria are dominant in pristine soils or sediments (Macnaughton et al., 1999; Saul et al., 2005) and it is known that they grow slowly in contrast to Pseudomonas spp. (Bouchez et al., 1997; Vildanova-Martsishin et al., 2002). Some Actinobacteria have been described to be able to degrade mineral oil hydrocarbons or related substrates (Greene et al., 2000). Rositz1 Ro106 had a similar sequence to clone LTUGr00156 from soil with aged hydrocarbon contamination (Iida et al., 2002; Kaplan & Kitts, 2004). However, others like Rositz1 Ro52, Rositz23 Ros5, Rositz23 Ros7 and Rositz23 Ros27 were similar to Propionibacterium acnes, which was also found in the brine-seawater interface (clone library KT-2K) (Eder et al., 2001). Rositz23 Ros6 and Rositz23 Ros120 were most similar to the sequence of Atopobium rimae (ATTC 49626) from the human oral cavity (Dewhirst et al., 2001; Paster et al., 2001). So far these genera did not appear of importance in biodegradation of mineral oil hydrocarbons.
Microbial diversity in a bioremediation system 49
FIG. 5: Maximum likelihood phylogenetic tree of Actinobacteria and Firmicutes sequences from the active stage of the Rositz samples.
Bacteroidetes: The small group of four Rositz clones, which represented Bacteroidetes, could all be affiliated to Flavobacterium spp. (Fig. 6). The phylogenetic distribution obtained two branches of these Rositz sequences, which were related to different Flavobacterium species, i.e. F. aquatile and F. gondwanense. Flavobacteria are chemo-organotrophic and has been isolated from soil and aquatic samples, but also from diseased fishes. Members of the genus Flavobacterium have previously been reported to increase in the mid phase of a bioremediation process on petroleum contaminated soil (Kaplan & Kitts, 2004). Sequences similar to the Rositz sequences were obtained from a well ventilated agricultural soil (clone S026) (Furlong et al., 2002), but also from groundwater with aged chlorophenol contamination (Männistö et al., 1999) and groundwater biofilms (Ross et al., 2001).
FIG. 6: Maximum likelihood phylogenetic tree of “Bacteroidetes” sequences from the active stage of the Rositz samples: The Actinobacteria sequences serve as the outgroup.
50 Microbial diversity in a bioremediation system
Firmicutes: The only sequence Rositz23 Ros53 related to Firmicutes (Fig. 5), was most similar to clone HuCB5 from human colonic samples (Holda et al., 2002). Overall, sequences obtained from the bioremediation system Rositz 3 had a wide phylogenetic distribution. Similar clones or strains were found in a variety of habitats, some of which were contaminated with mineral oil hydrocarbons and others were pristine. Representatives of nearly all related genera can degrade such contamination, according to the literature. But some genera, i.e. Zymomonas and Rhodoferax could be not found in soils contaminated with mineral oil hydrocarbons until now.
Effects of sequential nucleic acids extraction The initial hypothesis was that more sequences of bacteria which are hard to lyse would be found in extracts after several bead treatments. Because of their cell wall structure, gram-positive bacteria are harder to lyse than gram-negative bacteria (Bürgmann et al., 2001). However, sequences from gram-positive bacteria were as infrequent in the extract after three bead treatments of the soil sample as in the extract after the first bead treatment. Furthermore, the sequences from the two sequential extractions showed no significant difference of the phylogenetic distribution at first sight i.e. on the phylum or subphylum level (Table 1). A more detailed analysis, however, demonstrated that certain sequences were specific for one of the sequential extractions. Some genera were only detected in one of the two sequential extracts. For example Sphingomonas sequences were detectable predominately in the first extract and Zymomonas in the second/third extract. Sequences of genera, which were received from both extracts, often formed separate sequence clusters of the first bead treatment and of the second/third treatment. For example, P. anguilliseptica was typical for the first, and P. syringae was typical for the second extract. Acidovorax- related sequences also formed two clusters in which the extracts were represented to different degrees (Fig. 3). Different frequency of sequences in sequential extracts do not necessarily have to result from differences in cell-wall stability. Alternatively it may also be assumed that most of the nucleic acids extracted after the second bead
Microbial diversity in a bioremediation system 51 treatment originated from microorganisms located inside the soil particles and nucleic acids from the first bead treatment were obtained mostly from microorganisms located at the outside of the soil particles. Then the extraction of nucleic acids by sequential bead beating would have resulted in lyses of microorganisms from different soil niches i.e. inside or outside of a soil particle. Different degraders will coexist in contaminated soil and selection might be based among other factors on bioavailability of the contaminant (Friedrich et al., 2000; Grosser et al., 2000). Microorganisms inside soil particles might be adapted to degrade mineral oil hydrocarbons, which are adsorbed to natural organic matter and consequently available to fewer microorganisms than the free compounds. In addition, changes of the redox-potential were supposed to be the factor with strongest effect on the microflora upon contamination of soil (Fahy et al., 2005). Oxygen pressure inside soil particles will always be lower than in the outer layers of the particles, even in aerated soil systems like the one investigated here and therefore related bacteria with different frequencies in the sequential extracts might also differ in their response to oxygen (e.g. preferring microaerobic rather than aerobic conditions).
Comparison to other mineral oil hydrocarbon degrading communities Several investigations accumulated knowledge on the microbial diversity in soils, which were contaminated with mineral oil hydrocarbons (Evans et al., 2004; Kaplan & Kitts, 2004; Plaza et al., 2003; Radwan et al., 1995). On the one hand it is difficult to draw general conclusions on the microflora in soil from a small number of individual analyses considering the broad spectrum of possible pollutants, and the varying conditions with respect to age of the contamination, humidity of the soil, land use etc. On the other hand, rules on the occurrence of microorganisms appear to exist as, for example, Sphingomonas species were detected in all investigated soils with contamination of polycyclic aromatic hydrocarbons, independent of the geological and chemical properties of the soils and their geographic origin (Leys et al., 2004). The analyses of microbial diversity in mineral oil hydrocarbon contaminated soils on SSU rDNA level showed some differences to the SSU rRNA analysis of the Rositz heap in this study. Juck et al. (2000) found Actinobacteria being dominant in a cold-adapted bacterial community in petroleum hydrocarbon-
52 Microbial diversity in a bioremediation system contaminated soils with no degradation activity. Also Proteobacteria were abundant, in particular Gammaproteobacteria and Alphaproteobacteria. A number of sphingomonads were detected, but no pseudomonads, which was the dominating genus in the active stage of bioremediation heap Rositz 3. The bioreactor samples investigated by Mills et al. (2003) were originally amended with inorganic nutrients (NP 16:1) had a similar phylogenetic distribution as the Rositz sample. Pseudomonads and sphingomonads were most abundant. In contrast, Acinetobacter spp., which have also a known degradation potential for mineral oil hydrocarbons, Acidovorax spp. and Rhodoferax spp. were not detected. Kaplan & Kitts (2004) investigated a petroleum land treatment unit in which the soil heap was contaminated with 2440 mg petroleum hydrocarbon per kg soil, i.e. one third of the Rositz contamination. The authors analysed clone libraries from samples taken at day 14 (initial fast degradation phase) and at day 56 (slow degradation phase). The clone library of day 14 was dominated by Flavobacterium spp., Pseudomonas spp. and Azoarcus spp. and showed some differences to the Rositz analysis. Interestingly the analysis of the clone library of day 56 contained the same genera as found in Rositz soil, though the latter, because of its higher contamination level might still have been in an earlier degradation phase, perhaps one comparable with day 14 of the petroleum land treatment. Considering the differences in contamination, soil types and treatment procedures it is overall interesting to note that the rRNA-based analysis used in this study revealed several genera e.g. Pseudomonas, Sphingomonas, Acinetobacter, Acidovorax, and Flavobacterium as being probably important that have previously been reported to be relevant in rDNA-based studies.
Concluding remarks Abundant sequences in the clone libraries of the present study probably included those microorganisms, which were the main degraders of mineral oil hydrocarbons in this stage of bioremediation, because only the active microorganisms were detected. Even in culture experiments on complex media with diesel polluted samples and with such samples after addition of nutrients, respectively, it was shown that 26% and 68% of the cultured microorganisms were specialised in diesel degradation (Gallego et al., 2001).
Microbial diversity in a bioremediation system 53
There are many different niches available in soil, providing better conditions for many consortia involved in degradation. Not all members of a microbial community will be active in degradation at every time-point, but as in other systems (Kaplan & Kitts, 2004), a succession of the active portions of the community can be expected. At a later point in the bioremediation process other bacteria would have been detectable in the soil heap Rositz 3. Further investigation on bioremediation system Rositz 3 should verify the presence of the so far detected microorganisms at other time points of the process. The analysis of SSU rRNA clarified the identity of the microorganisms, but it provided no direct evidence for their function in the soil or their metabolic potential. Determination of marker genes for degradation of mineral oil hydrocarbons will be necessary to address this aspect.
ACKNOWLEDGEMENTS
We thank Mr. Hagedorn (Dierichs & Hagedorn Consulting GmbH Freiberg) and Mr. Seelig (Bodensanierungsgesellschaft mbH Seifersbach) for providing samples and results of their chemical analyses. Nicole Popp is a recipient of a PhD fellowship from the Deutsche Bundesstiftung Umwelt. Michael Schlömann acknowledges generous long-term support from the Deutsche Bundesstiftung Umwelt.
3
Quantification of bacterial groups and of catabolic genes in bioremediation systems for mineral oil hydrocarbon contaminated soil
Nicole Popp1, Michael Schlömann1 and Margit Mau1
1 Interdisziplinäres Ökologisches Zentrum, TU Bergakademie Freiberg, Leipziger Str. 29, D-09599 Freiberg, Germany
Running title: Microbial degradation potential in a bioremediation system
Correspondence: Michael Schlömann [email protected] 56 Microbial degradation potential in a bioremediation system
ABSTRACT
In this study the degradation potential of the indigenous microflora of a mineral oil hydrocarbon contaminated bioremediation system and the occurrence of typical bacterial groups of an active bioremediation system were determined and compared to those in less active bioremediation stages and in other aerated soil remediation systems. The bioremediation was performed off-site in heaps of soil. As representative degradation genes for mineral oil hydrocarbons the genes for alkane hydroxylase (alkB) and for catechol 2,3-dioxygenase (C23O) for aromatic compounds were quantified on DNA level by competitive PCR. More copies of genes for AlkB than for C23O could be detected in soil. Comparisons of results from two sequential nucleic acid extractions revealed that in most cases copy numbers were higher in the first of the two sequential extracts. The copy numbers of the catabolic genes remained nearly at the same level during the technical process. The quantification of the main bacterial groups on RNA level were performed by membrane hybridisation with specific probes. Probes were chosen from the literature for the genera Sphingomonas, Pseudomonas, Acidovorax and Acinetobacter. New probes were designed in this study for genera Thiobacillus and Rhodoferax and for group clone TRS13. The investigated bacteria were already at high levels before the start of the process and they stayed at high level when the active biodegradation process began. Later on the initially most common bacterial groups dropped dramatically. In most cases, there was a higher percentage of the individual genera in the first of the sequential extracts. The genera Sphingomonas and Thiobacillus were detectable in all investigated samples. For the estimation of the degradation potential in contaminated material and for monitoring of the remediation success quantification of the catabolic genes seems more suitable than the detection of the main taxonomic groups.
INTRODUCTION
Mineral oil hydrocarbons and individual compounds of mineral oil hydrocarbons are ubiquitous in the environment. They were generated by geochemical processes or were even produced by living organisms (van Beilen et al., 2003). Because of inappropriate transport, storage and treatment processes the
Microbial degradation potential in a bioremediation system 57 enormous human consumption of petroleum, gasoline and related products lead to the contamination of soil and groundwater. Further use of such contaminated environmental compartments is only possible after their decontamination. The most frequent solution for contaminated soils is bioremediation, a technology that utilizes the metabolic potential of indigenous microflora for cleaning the contaminated site (Watanabe, 2001). Many physicochemical and biological parameters including e.g. the bioavailability of pollutants, the soil characteristics as well as the presence of nutrients and oxygen influence the microbial degradation of mineral oil hydrocarbons (Friedrich et al., 2000; Margesin & Schinner, 1997). Probably under close to natural conditions indigenous microorganisms tend to become predominant compared to laboratory strains, because they are better adapted to the respective environment. One important remediation strategy for mineral oil hydrocarbon contaminated soils is the off-site remediation as in this investigation, in which the soil formed a heap. At the beginning of biostimulation the soil may be fertilized with mineral fertilizer and during the degradation process the heap is usually ventilated. The temperature increase in soil may reflects the degradation activity of the indigenous microflora (Condé & Hagedorn, 1997), which uses the mineral oil hydrocarbons as carbon and energy source. The degradation potential of the microbial community has a crucial influence on the success of bioremediation attempts and therefore it can be advantageous to assess this potential. Since for off-site bioremediation in heaps needs to precede fast to be economic, only aerobic pathways have to be considered for an assessment of the degradation potential. There are various possibilities with different force of expression for the detection of the active microflora and of the degradation potential in such a bioremediation process. The cultivation of microorganisms does not reflect the real microbial community composition, because fast growing and high nutrient concentration accepting microorganisms grow preferrencially (Felske et al., 1999). This problem could be circumvented with cultivation independent methods. DNA based methods, like PCR, illustrate the genetic potential of a sample (Lüdemann et al., 2000). Active as well as inactive microorganisms are detected. For the detection of the active portion of a microbial community RNA
58 Microbial degradation potential in a bioremediation system based methods are necessary (Lüdemann et al., 2000). Furthermore not only the detection of the active bacterioflora and the degradation potential but also the quantification of them are important for the understanding of the process. The analysis of SSU rRNA of soil samples is now a well established method (Felske et al., 2000; Nogales et al., 1999) and the main bacterial groups in such an active system were also characterised by sequencing analysis of cDNA of the bioremediation heap Rositz 3 (Popp et al., in preparation). Despite of possible nucleic acid extraction, PCR and cloning biases (Kozdroj & van Elsas, 2001) these groups should be the main degraders in the active bioremediation system Rositz 3. The quantification of these bacterial groups throughout the technical process was performed by membrane hybridisation with specific probes. In contrast to rRNA detection the determination of the mRNA level of specific genes in soil samples is not straightforward, because the extraction of mRNA is in fact possible but very tricky, not always reliable and the amount of extracted mRNA is often too low for quantitative investigations (Bürgmann et al., 2003). Therefore, we decided to detect the degradation genes by PCR amplification on the DNA level, where active and inactive microorganisms are detected. Competitive quantitative PCR with an internal standard was applied for quantification of the key degradation genes during the course of bioremediation. Mineral oil hydrocarbons largely consist of alkanes and aromatic compounds (Hellmann, 1995). Therefore their degradation may be considered as representing that of mineral oil hydrocarbons. The initial step of aerobic degradation of (long chain) alkanes is catalysed by alkane hydroxylase (AlkB; EC 1.14.15.3). This enzyme or its gene, respectively, are the best targets for the assessment of biodegradation potential since alcohol, fatty acids and other intermediates of this degradation pathway may also be the result of other degradation processes in soil (van Beilen et al., 2003). Of the primer pairs, which have previously been reported for the amplification of the alkane hydroxylase genes, some are specific to alkane hydroxylases of specific degradative strains (Smits et al., 2002; Whyte et al., 2002a), but degenerate primers have been described also, which detect alkane hydroxylases of a broad range of degraders, Gammaproteobacteria and Betaproteobacteria as well as Actinobacteria (Smits et al., 1999). The latter primers were considered to be
Microbial degradation potential in a bioremediation system 59 preferable to analyse the degrading microbial community in a bioremediation heap. A representative enzyme for aerobic degradation of various aromatic compounds is catechol 2,3-dioxygenase (meta-cleavage) (C23O; EC 1.13.11.2) (Smith, 1990). Primers for C23O genes have mainly been described for subfamily I.2.A, which represents Pseudomonas spp. (Eltis & Bolin, 1996; Meyer et al., 1999; Okuta et al., 1998). The aim of this study was the quantification of the main bacterial groups and the catabolic genes during the course of bioremediation in heap Rositz 3. In comparison to data from other bioremediation systems these molecular parameters should clarify, if there are differences between various soil materials. Furthermore, it is of interest, whether it is possible to differentiate material of well and slowly degrading heaps and if the investigated parameters can give evidence for the success of the bioremediation prior to setting up a heap.
MATERIALS AND METHODS
Soil samples The investigated silty and partially loamy soil material originated from the contaminated site Rositz (Thuringia; Germany), where a tar factory was processing brown coal tar between 1917 and 1990. The factory was destroyed in World War II, which is still the main reason for the contaminated soil in this area. The contamination consists of alkanes, BTEX- and polyaromatic compounds as well as phenols (Verwaltungs- und Verwertungsgesellschaft Industriegelände Rositz mbH, 1998), which remained on-site for more than 60 years. Soil samples of three bioremediation heaps with different amounts of mineral oil hydrocarbons were investigated. The bioremediation heaps Rositz 1 and Rositz 2 were each composed of 1100 t mineral oil contaminated soil material, with a basis of 80 m² and 3.5 m height and Rositz 3 was 700 t, with a basis of 60 m² and 3.5 m height. The concentration of the mineral oil were for Rositz 1: 2000 mg per kg soil dry weight; for Rositz 2: 4000 mg per kg soil dry weight and for Rositz 3: 8000 mg per kg soil dry weight. Half a ton mineral fertiliser (NPK 15:15:15) was added to 1000 t soil and all heaps were permanently ventilated (Condé & Hagedorn, 1997).
60 Microbial degradation potential in a bioremediation system
The time points of sampling were chosen on the basis of the temperature development in the heaps (Fig. 1) and according to the chemical analyses (Table 1). Rositz 1 and Rositz 2 samples from the beginning, from the active time period and from the end of the bioremediation process were investigated. For Rositz 3 the investigated samples were taken at six time points during the course of bioremediation. The active time period was defined as the point, when, according to chemical analyses (Rositz 1: 1350 mg, Rositz 2: 1660 mg, Rositz 3: < 6000 mg at time of sampling), the bioremediation system had started to degrade the mineral oil hydrocarbons (Table 1, Fig. 1a, Fig 1b). The samples were collected from the bioremediation systems with a special drill for manual soil sampling from 1.5 m height and 0.75 m into the system. Samples were taken at several points in an average distance of 2 m along the heaps. Soil material from each sampling point was collected separately into a 50 ml reaction tube and frozen at –80 °C.
TABLE 1. Bioremediation stages, temperature pattern, mineral oil hydrocarbon concentration, holding time and samples numbers for the Rositz bioremediation heaps
Mineral oil Bioremediation hydrocarbon Heap Ambient Treatment stages during concentration Sample Soil heap temperature temperature time of the the technical (mg mineral oil number* (°C) (°C) heap (days) process per kg soil dry weight) Rositz 1 at the beginning 12 2 2000 1 1-10 active stage 11 11 1350 142 35-39 at the end 22 17 700 200 53-57 Rositz 2 at the beginning 13 0 4000 1 17-20 active stage 9 11 1660 128 40-45 at the end 15 17 970 186 58-63 Rositz 3 at the beginning 12 8 8000 1 30-34 active stage† 21 11 < 6000 43 46-52† active stage‡ 26 22 - 101 64-70‡ active stage‡ 21 4 3300 240 71-77‡ active stage‡ 23 16 - 442 78-85‡ at the end 34 24 980 498 86-93 Grimma at the beginning 18.5 14 2900 1 3-11 Espenhain at the beginning 5 - 9600 – 15200 1 1-10 * sample mixture of individual samples taken along the heap at this time point † sample 49 was used for sequencing analysis to detect the degraders ‡ active stages of bioremediation of the heap with changed degradation spectrum, because of previous degradation activity of the indigenous microflora
Microbial degradation potential in a bioremediation system 61
36 9000 active stage of bioremediation 32 8000
28 7000
Rositz 1 24 6000 53-57
20 5000
Rositz 2 hydrocarbons ral oil
16 4000 35-39; 1-10 58-63 40-45 temperature (°C) temperature 12 3000 (mg/kg soil dry weight)
17-20 8 2000 concentration of mine of concentration
4 1000
0 0 3.12 12.1 21.2 2.4 12.5 21.6 31.7 9.9 19.10 28.11 7.1 16.2 28.3 7.5 16.6 26.7 length of time of bioremediation process of heaps Rositz 1 and Rositz 2 (03/12/01 - 20/06/02)
36 9000 active stage of 86-93 bioremediation 32 8000
28 7000 64-70
78-85 24 6000 71-77 46-52
20 5000 ral oil hydrocarbons ral oil
16 4000 temperature (°C) temperature 30-34 12 3000 (mg/kg soil dry weight)
8 2000 concentration of mine of concentration
4 1000
0 0 3.12 12.1 21.2 2.4 12.5 21.6 31.7 9.9 19.10 28.11 7.1 16.2 28.3 7.5 16.6 26.7 length of time of bioremediation process of heap Rositz 3 (12/03/02 - 29/07/03)
FIG. 1 (a): Temperature development of heaps Rositz 1 and Rositz 2. The interruptions in the curves correspond to a shut down of the bioremediation systems caused by a turn over of the soil material (April 2002). Grey rhombus ( ): temperature pattern of heap Rositz 1; grey square ( ): temperature pattern of heap Rositz 2; transparent triangles ( ): mineral oil hydrocarbon concentration of heap Rositz 1; transparent circle ( ): mineral oil hydrocarbon concentration of heap Rositz 2; black circle (●): number of the sample at the respective time point (Table 1). FIG. 1 (b): Temperature development of heaps Rositz 3. The interruptions in the curves corresponds to a shut down of the bioremediation systems caused by low temperature and a turn over of the soil material (Dec. 2002). Grey triangle ( ): temperature pattern; transparent circle ( ): mineral oil hydrocarbon concentration; black circle (●): number of the sample at the respective time point (Table 1).
62 Microbial degradation potential in a bioremediation system
Furthermore, mineral oil hydrocarbon contaminated soil materials from two other bioremediation systems were investigated at the beginning of their bioremediation process (Table 1). These soils originated from oil tank storage areas in former Russian military bases. Grimma, material was of 800 t mineral oil contaminated sandy soil (2900 mg mineral oil per kg soil dry weight), and Espenhain material was 1200 t mineral oil contaminated soil (9600 - 15200 mg mineral oil per kg soil dry weight) (Saxony; Germany). The soil material from Espenhain was very moistly and had a very loamy and partially clayey structure. Fifty kg mineral fertiliser (NPK 15:15:15) were added to 1000 t soil of bioremediation system Espenhain and both bioremediation systems were permanently ventilated (Condé & Hagedorn, 1997).
Strains and cultivation conditions The transformation experiments were performed with Escherichia coli DH5α (Gibco-BRL). The strain was grown on Luria-Bertani (LB) medium (1 % tryptone, 0.5 % yeast extract, 1 % NaCl, 1.5 % agar) with 50 µg ampicillin ml-1 at 37 °C (Sambrook & Russell, 2001). Reference strains for detection of the degradation genes and for membrane hybridisation and their cultivation conditions are listed in Table 2.
Nucleic acid extractions Nucleic acid extraction from the soil samples. A beat beating method with subsequent phenol extraction, which allowed the simultaneous extraction of RNA and DNA (Görres, 2001) was applied in a modified way as described previously (Popp et al., in preparation). The extracts of the first bead treatment were handled separately from the second/third bead treatment. The nucleic acid extracts from 0.5 g soil were resuspended in 50 µl deionised water and stored at –20 °C. Nucleic acid extracts of the first bead treatment were designated according to the sample number e.g. Rositz 30-34, and those of the second/third bead treatment were designated by the same name and an asterisk e.g. Rositz 30-34*. 30-34 were the sample numbers of one sampling point (Table 1).
Microbial degradation potential in a bioremediation system 63 , 1989) , 1990) , 1995) , 1966) , 1996) , 1966) unpub.) et al.
et al. et al. et al. et al. et al. (Anders (Wettstein, unpub.) (Davis, 1969) (Juni, 1972) (Lindner, (Erler, unpub.) (Spring (Stanier (Reichelt, 1973) (Horvath (Feist & Hegeman, 1969; Stanier Reference or source (Juni & Janik, 1969) (Gorlatov
Growth Temperature 30 °C 30 °C 30 °C 30 °C 25 °C 30 °C 30 °C 30 °C RT* RT* RT* 30 °C RT*
, et al. (DSM -hexadecane n with glucose
, † † , 1998; Smits X (DSM medium 98) (1997)) et al. et al.
† ‡ ‡ ‡ ‡ ‡ ‡ Sphaerotilus-Leptothrix Rhizobium solid 2 mM benzoate minimal medium (2 g/l) and glycin (20 (modification of Eulberg solid LB DIN EN ISO 11348-1 solid LB solid LB solid LB solid LB solid LB solid medium 803) liquid R2A (DSM medium 830) solid Medium solid minimal medium was supplied through the gas phase (Ratajczak 1999). liquid minimal medium
G tion of the degradation genes and for membrane hybridisation probe SPH120 probe LSB145 probe Ros72-194 probe Ros23-121 probeRos40-63 Negative control for probe Ac probe PSM
G probe SPH120 probe Ac probe LSB145 Positive control for AlkB AlkB C12O C23O probe PSM
FG1 FG1 DSM 65 mt2 ATCC 1CP ADP1 DSM 10617 DSM 6898 sp. AZD5_G10 sp. DSM 587 DSM 7151 sp. Amico73 Reference strains and cultivation conditions for detec (1974) et al. Strain Acinetobacter baylyi Rhodococcus opacus Pseudomonas putida Pseudomonas putida 12633 Vibrio fisheri Sphingomonas herbicidovorans DSM 11019 Paracoccus denitrificans TABLE 2. Acinetobacter Bradyrhizobium * room temperature † Dorn ‡Luria-Bertani medium (Sambrook & Russell, 2001) Acidovorax Janthinobacterium lividum Leptothrix mobilis Azoarcus evansii
64 Microbial degradation potential in a bioremediation system
Nucleic acid extraction of reference strains for detection of degradation genes. The extraction was carried out by the method of Wilson (1987). The extraction of Rhodococcus opacus 1CP nucleic acid was done without the cetyl trimethyl ammonium bromide (CTAB)/NaCl step. The nucleic acid pellets were resuspended in 100 µl deionised water and stored at +4 °C. For RNA digestion, the nucleic acid extracts were incubated in the presence of DNase free RNaseA (Sigma) at a final concentration of 10 µg ml-1 for 1 h at room temperature (Sambrook & Russell, 2001). Nucleic acid extraction of the reference strains for membrane hybridisation. The same extraction method as for the soil samples was applied (Görres, 2001; Popp et al., in preparation), but the cell pellets were treated only once with the bead beater (MM200, Retsch) and the extracts were only once extracted with 1 ml phenol:chloroform:isoamyl alcohol (25:24:1 v/v/v). The nucleic acid pellets were resuspended in 100 µl deionised water and stored at –20 °C. The success of nucleic acids extraction was verified by an 1 % agarose gel and ethidium bromide staining or by an urea denaturing 4.5 % PAA gel and silver staining (Schumacher et al., 1983).
Separation of RNA and DNA Separation of RNA and DNA for soil samples was performed with the nucleic acid binding silica spin-columns of an Invisorb TwinSpin Cell Mini Kit (Invitek) using the buffers supplied by the manufacturers. The protocol was modified as follows: Lysis Solution T, which is meant to lyse cells, was added to the nucleic acid extracts from soil to obtain the optimal reaction conditions for the spin columns. At the end, the elution of the RNA and the DNA from the binding-spin filter were carried out twice with smaller volumes of water to improve elutions capacity. The RNA extracts were stored at –20 °C and the DNA extracts at +4 C. Individual samples of time point were extracted separately and extracts of DNA and RNA, respectively were combined after extraction and precipitated with sodium acetate and ethanol (Moore, 1987). The precipitated nucleic acid was resuspended in 100 – 120 µl deionised water.
Microbial degradation potential in a bioremediation system 65
Quantification of RNA, DNA and plasmid concentration RiboGreen RNA reagent (Molecular Probes) was used to assay RNA concentration and PicoGreen dsDNA reagent (Molecular Probes) for measurement of DNA concentration. Fluorescence measurements were performed with a TBS-380 Mini-Fluorometer (Turner BioSystems) using the protocols of Molecular Probes. The photometric measurement of plasmid concentration at 260 nm (Sambrook & Russell, 2001) was performed with a Cary50Bio UV-Visible spectrophotometer.
PCR conditions for detection of catabolic genes The used primers are listed in Table 3. The 25 µl reaction mixtures contained 1 x PCR Master Mix (ABgene) resulting in 0.2 mM dNTP, 75 mM Tris-HCl
(pH 8.8), 20 mM (NH4)2SO4 and 1.5 mM MgCl2. Positive controls contained 0.5 µl 1:10 diluted DNA extract from pure cultures. For the amplification of the degradation genes in environmental samples 1 µl DNA extract from environmental samples was used in the PCR reaction. For negative controls the reactions were prepared without DNA extract. All PCR products were visualised by an agarose gel electrophoresis and ethidium bromide staining. For verification, the PCR products for catabolic genes of positive controls were cloned and sequenced with labelled primer T3 (5‘-CGCGCAATTAACCCTCACTAAAG-3‘) and labelled primer T7 (5’-GCGCGTAATACGACTCACTATAG-3’) (Stratagene).
TABLE 3. Oligonucleotides used for PCR experiments Approx. Annealing Oligo- expected Nucleotide sequence Specificity Reference temperature nucleotide product 5’ → 3’ (°C) size (kb)
TS2S* AlkB 0.55 AGC TCA YGA RYT RGG TCA YAA G (Smits et al., 50 TS2Smod* AGC TCA YGA RIT IGG ICA YAA R 1999) TS2Smod2* AGC TCA YGA RIT ITC ICA YAA R deg1RE† ATT CGC RTG RTG RTC IGA RTG deg1RE2† ATT CGC RTG RTG RTC RCA RTG catA-G(+)-fw1 C12O 0.3 GCA SCA TCG ARG GSC CST AYT A (Thiel, unpub.) 50 catA-G(+)-rev1 CAG GTG SGC SGG 5CG CC
23DOF C23O 0.75 ATG GAT DTD ATG GGD TTC AAG GT (Meyer et al., 50 23DOR ACD GTC ADG AAD CGD TCG TTG AG 1999) * forward primers † reverse primers
66 Microbial degradation potential in a bioremediation system
PCR conditions for detection of alkB: All possible combinations of primers were tested (Table 3). Primer concentration in the reactions was 0.2 µM of each primer. For reamplification PCR the same final concentrations were used but 1 µl of the PCR product from the first PCR served as the template. The PCR program comprised the following steps: 4 min at 95 °C, 25 cycles (45 s at 95 °C, 1 min at 50 °C, 1 min at 72 °C), 5 min at 72 °C (modified from Smits et al. (1999)). PCR conditions for detection of genes for C12O: Primer concentration in the reactions was 0.5 µM of each primer. The following PCR program was performed: 5 min at 95 °C, 10 cycles (45 s at 95°C, 30 s at 60 °C, 1 min s at 72 °C – annealing temperature decrease 1 °C per cycle), 20 cycles (30 s at 95°C, 30 s at 50 °C, 1 min at 72 °C), 5 min at 72 °C (Thiel, unpub.). PCR conditions for detection of genes for C23O: Primer concentration in the reactions was 0.5 µM of each primer and 0.75 mM more MgCl2 (MBI Fermentas) was added. The following PCR program was used: 5 min at 94 °C, 35 cycles (15 s at 94°C, 30 s at 50 °C, 45 s at 72 °C), 3 min at 72 °C (modified from Meyer et al. (1999) and Wikström et al. (1996)).
Preparation of competitor for competitive quantitative PCR Internal standards resulting in 50 bp – 100 bp longer PCR products than the wild-type PCR product were prepared for competitive quantitative PCR (Wang & Mark, 1990). For preparation of competitors, the cloned PCR products of Acinetobacter baylyi ADP1 in pAlkB-FP01 was used for alkB genes and those of Pseudomonas putida mt2 in pC23O-FP02 for genes of C23O (Table 4). Suitable restriction endonucleases, which cut the PCR products once or twice were identified by using the program MapDraw (DNASTAR, Lasergene).
Microbial degradation potential in a bioremediation system 67
TABLE 4. Plasmids used in this study
Plasmid Relevant characteristics Source
r 1 pBluescript II SK (+) Plasmid derived from pUC19; Plac lacZ’ Ap *; f1(+) origin; colE1 origin (Stratagene)
2 pAlkB-FP01 0.55-kb alkB PCR fragment of A. baylyi ADP1 in EcoRV site of 1 This study
3 pC23O-FP02 0.75-kb C23O PCR fragment of P. putida mt2 in EcoRV site of 1 This study
4 pCompAlkB-FP03† 0.55-kb alkB PCR fragment of A. baylyi ADP1 including a 59 bp VspI This study fragment of 1 in 2
5 pCompC23O-FP04‡ 0.75-kb C23O PCR fragment of P. putida mt2 including a 59 bp VspI This study fragment of 1 in 3 * Apr plasmid carries information for ampicillin resistance † preparation of this plasmid is visualised in Fig. 2 ‡ preparation of this plasmid is visualised in Fig. 3
A 59 bp fragment was obtained by VspI digestion of pBluescript II SK(+) vector (Stratagene) and was introduced into both competitors. Since it is difficult to extract 50 – 100 bp fragments from agarose gels, the cloning of the larger fragments from pBluescript II SK (+) was avoided by an additional digestion with Alw44I (Fig. 2).
FIG. 2: Restriction sites of restriction endonucleases VspI and Alw44I in pBluescript II SK (+)
Preparation of competitor for genes of AlkB (pCompAlkB-FP03): At first pAlkB-FP01 was digested with BssHII. The fragments with sizes of 2800 bp and 750 bp were separated on an agarose gel and eluted from the gel with EasyPure (Biozym). The 2800-bp fragment was dephosphorylated with shrimp alkaline phosphatase (Roche) and again purified with EasyPure (Biozym). The 750-bp fragment was digested with VspI and the resulting fragments were also purified with EasyPure (Biozym). Finally, the 2800-bp vector fragment, the VspI- digested 750-bp fragment and the fragments from VspI-Alw44I-digested pBluescript II SK(+) were cloned (Fig. 3; Fig. 2; Table 4).
68 Microbial degradation potential in a bioremediation system
FIG. 3: Construction of competitor (pCompAlkB-FP03) for competitive quantitative PCR of genes of AlkB. The figure shows the restriction patterns and the resulting ligation products for construction of pCompAlkB-FP03 as a competitor, which results 59-bp longer PCR fragment than the gene to be detected.
Preparation of competitors for genes of C23O (pCompC23O-FP04): Plasmid pC23O-FP02 with the cloned C23O-PCR fragment from A. baylyi ADP1 was digested with NdeI. The linearised fragment was dephosphorylated with shrimp alkaline phosphatase (Roche), loaded onto an agarose gel and eluted with EasyPure (Biozym). Finally, this fragment and the fragment from VspI-Alw44I-digested pBluescript® II SK(+) were cloned (Fig. 4; Fig. 2; Table 4).
Microbial degradation potential in a bioremediation system 69
FIG. 4: Construction of competitor (pCompC23O-FP04) for competitive quantitative PCR of genes of C23O. The figure shows the restriction patterns and the ligation products of pCompC23O-FP04 as internal standard, which results in 59-bp longer PCR fragment than the gene to be detected.
After transformation, white clones were screened for inserts by PCR with the primer combination T3 (5’-CGCGCAATTAACCCTCACTAAAG-3’) and T7 (5’-GCGCGTAATACGACTCACTATAG-3’). The 25 µl reaction mixtures contained the following components and final concentrations: 1 x PCR Master Mix (ABgene), 0.24 µM of each primer and a small amount of biomass of a white colony picked with a toothpick. For negative controls the reactions were prepared without biomass. The following PCR program was used: 3 min at 94 °C, 30 cycles (30 s at 94 °C, 30 s at 55 °C, 1.5 min at 72 °C), 5 min at 72 °C. Additionally the obtained PCR products were digested with VspI to test for the introduced 59 bp fragement. Furthermore, a PCR with the specific primers for genes of AlkB and C23O (Table 3) was carried out to check the primer binding sites in the competitors. The PCR conditions were as described above.
PCR conditions for competitive quantitative PCR Conditions for genes of AlkB. PCR tests on environmental samples were not successful in combinations with primer TS2Smod2 (Table 3). Therefore, this
70 Microbial degradation potential in a bioremediation system primer was not used for this application. The other forward primers and reverse primers were mixed in a 1:1 ratio (Table 3). The 25 µl reaction mixtures for competitive quantitative PCR contained the following components and final concentrations: 1 x PCR Master Mix (ABgene), 0.5 µM of each primer mix, 0.1 µg BSA µl-1 (MBI Fermentas), 5 % (v/v) DMSO (Roth) and 1 µl DNA extract of the environmental sample as well as 0.5 µl DNA of the competitor in the respective dilution. For reamplification PCR the same final concentrations were used, but 1.5 µl of the PCR product from the first PCR served as the template. For negative controls the reactions were prepared without DNA extract. The PCR program used was described above. Conditions for genes of C23O. The primers used are listed in Table 3. The composition of the reaction mixture was as described for alkB genes but with
0.75 µM of each primer and additionally 0.75 mM MgCl2 (MBI Fermentas). Several PCR reactions were performed with different dilution levels of the competitors. Bands of the competitor and the PCR fragment were separated by 2 % agarose gel electrophoresis and reactions with the same signal intensity of the two bands were chosen for the calculation of the copy number in the respective environmental sample.
Calculation of copy numbers per ng DNA extract of environmental samples The number of base pairs of the competitor plasmids were 3568 bp for genes for AlkB and 3741 bp for genes for C23O. The molar mass of one nucleotide pair was assumed to be 662 g mol-1 (Sambrook & Russell, 2001). The molar mass of competitors was calculated as the number of base pairs multiplied by the molar mass of one nucleotide pair and was estimated to be 2362016 g mol-1 for genes of AlkB and 2476542 g mol-1 for genes of C23O. The copy number of competitor per µl was calculated by the following equation:
⎛ng ⎞ DNA concentration ⎜ ⎟ ∗ Avogadro's number ()1 copy number of competitor ⎝ μl ⎠ mol ⎜⎛ 1 ⎟⎞ = ⎝ μl ⎠ ⎛ng ⎞ per volume molar mass of competitor ⎜ ⎟ ⎝ mol ⎠
Microbial degradation potential in a bioremediation system 71
The copy number of undiluted competitor suspension was 2,8 x 1010 µl-1 for genes of AlkB and 1,7 x 1010 µl-1 for genes of C23O. To calculate copy numbers in the DNA extract of environmental sample the copy number of competitor per µl was multiplied by the dilution factor. To compare individual environmental samples, the copy number of DNA extract of environmental sample per µl was divided by DNA concentration of environmental sample.
copy number per volume ⎛ 1 ⎞ ⎜ μl ⎟ copy number per mass DNA ⎛ ⎞ of DNA extract of environmental sample ⎝ ⎠ ⎜ 1 ⎟ = ⎝ ng ⎠ ⎛ng ⎞ extract of environmental sample DNA concentration of environmental sample ⎜ ⎟ ⎝ μl ⎠
Probe selection for membrane hybridisation The probes for membrane hybridisation were selected based on a diversity analysis of a sample from the active time period of the bioremediation system Rositz 3 (Popp et al., in preparation). Probes for the main detected bacterial groups were either chosen from literature or a specific probe was designed by applying the probe-design function of the ARB software (Ludwig et al., 2004). All probes and the corresponding references are listed in Table 5. The bacterioflora was detected by probe EUB338 (Amann et al., 1990). The
Pseudomonas probe PSMg and the Acidovorax probe LSB145 had no mismatch to the relevant Rositz sequences. The 12 sequences of Rositz Sphingomonas group (Popp et al., in preparation) had one mismatch with the Sphingomonas probe SPH120. All other Sphingomonas-like Rositz sequences had a perfect match with the probe. A comparison of probe Ac for genus Acinetobacter with Rositz sequences was not possible. The probe binds on SSU rRNA positions 836-858 bp, while the Rositz sequences included only the first 500 bp of SSU rRNA. However, nearly all other Acinetobacter like sequences in the database matched with this probe. The probes for genera Thiobacillus and Rhodoferax as well as for the clone TRS13-related group were designed using the ARB software and had a perfect match with the relevant Rositz sequences. The mfold web server was used for checking the secondary structure of the probes designed in this study (Zuker, 2003). To test the probes, RNA extracts of suitable positive and negative control strains (Table 2) were used. Negative control strains were chosen to have a SSU rRNA with 1 or 2 mismatches with
72 Microbial degradation potential in a bioremediation system the probe sequence. Because no negative control with less mismatches was available, Pseudomonas putida ATCC 12633 was used as negative control with six mismatches for probe Ac (Kenzaka et al., 1998).
TABLE 5. Probes used for membrane hybridisation Hybridisation Nucleotide sequence Reference Probe Specificity for temperature 5' → 3' for probe (°C) (Amann et al., EUB338 Bacteria GCT GCC TCC CGT AGG AGT 50 1990) (Braun-Howland PSM Pseudomonas spp. CCT TCC TCC CAA CTT 54 G et al., 1993) (Kenzaka et al., Ac Acinetobacter spp. GCG CCA CTA AAG CCT CAA AGG CC 52 1998) Ros40-63 Clone TRS13 TAT TGC TAC CCC CGC TCG this study 54 SPH120 Sphingomonas spp. GGG CAG ATT CCC ACG CGT (Neef, 1997) 54 LSB145 Acidovorax spp. CTT TCG CTC CGT TAT CCC (Huber, 1997) 54 Ros72-194 Rhodoferax spp. TTG CGA ATC CCC CGC TTT this study 50 Ros23-121 Thiobacillus spp. CTC GGT ACG TTC CGA CGC this study 54
In vitro transcription No suitable positive controls were available for newly designed probes of this study, because they are targeted against environmental sequences. Therefore, in vitro transcription was used for synthesis of strand-specific RNA sequences. Template DNA of clones Rositz1 Ro9, Rositz1 Ro68 and Rositz23 Ros12 (Popp et al., in preparation) was linearized with SmaI, which cuts the pBluescript II SK(+) vector in the multi cloning site downstream of the ligated SSU rRNA fragments and does not cut SSU rRNA fragments. In vitro transcription was done with T7 RNA Polymerase (MBI Fermentas) applying the protocol of the manufacturer.
Dot blot hybridisation A dot blot hybridisation manifold model DHM-96 (Scie-Plas) was used. Positive and negative controls were blotted as dilution series starting with 128 ng RNA extract in 50 µl of RNA solution per dot and twofold dilutions down to one ng RNA extract in 50 µl of RNA solution per dot. All controls and almost each environmental sample was blotted in triplicate on each membrane to reduce statistical error. 50 ng of RNA extract of the environmental samples in 50 µl of RNA solution were blotted per dot. The hybridisation procedure of Raskin et al. (1997) was applied to quantify specific genera. Signals from each sample were
Microbial degradation potential in a bioremediation system 73 compared with signal intensities from dilution series of reference RNAs. Both were blotted onto the same membrane. The abundance of these genera in the microbial community was expressed as the percentage of the total SSU rRNA in the sample. Using the specific and in this case the bacterial probe EUB338 (Amann et al., 1990) as an universal probe, the specific and the total SSU rRNA concentrations were determined. Therefore, two independent hybridisations were necessary. To determine the ratio of quantifications with probe EUB338 and the specific probe the same dilution series of reference RNAs were blotted onto the membranes used in the respective hybridisation. Denaturation of RNA was achieved by adding 3 volumes of 7 % (w/v) glutardialdehyde solution (Roth) to the nucleic acids extract and by incubating the mixture for 10 min at room temperature. The samples were than diluted with water to the intended volume, half volume was removed for blotting and the remainder was further diluted with deionised water containing traces of loading dye (MBI Fermentas). The strategy of Raskin et al. (1997) was applied for membrane hybridisations. The hybridisation conditions were optimised with respect to temperature to achieve clear-cut results with positive and negative controls, respectively. Generally, a “low” hybridisation temperature was chosen for the hybridisation, while the washing steps of the membrane had stringent temperature conditions. The wash temperature with the best result was chosen for hybridisation. The composition of the hybridisation and buffer solution was described by Raskin et al. (1994), but blocking reagent was substituted for Denhardt solution. One hundred µM fluorescein-labelled probe was added to 50 ml hybridisation solution (0.9 M NaCl, 2 % blocking reagent, 50 mM phosphate buffer (pH 7.2), 5 mM EDTA, 0.5 % SDS). The washing buffer contained 1 x SSC (0.15 M sodium chloride, 15 mM sodium citrate) and 0.1 % SDS. 4.5 U Anti-Fluorescein-AP, Fab fragments (Roche) in 20 ml detection buffer (100 mM Tris/HCl (pH 9.5), 300mM NaCl) were used to detect probes according to the procedure described by the manufacturer. CDP-Star (Roche) was diluted 1:100 (v/v) in detection buffer and the chemiluminescence was detected for a maximum 90 min with a Chemilumenescince detection unit (Syngene). The success of the membrane hybridisation was verified by including negative control, whose signal always remained below the signal of the environmental samples.
74 Microbial degradation potential in a bioremediation system
Analysis of dot blot hybridisation The program tool “array analysis” of TotalLab (Nonlinear Dynamics) was used to quantify chemiluminescence signals. The signal intensity and the known amounts of blotted standards allowed the calculation of saturation curves for all probes. The linear range of these curves were used for calculation of the detected amounts (ng) of RNA from the environmental samples. The ratio of the amounts of RNA detected by a specific probe to the amounts of RNA detected with the bacterial probe EUB338 (Amann et al., 1990) were expressed as contribution of the probe-targets to the bacterial microflora.
RESULTS
Quantification of the key degradation genes Since the main components of mineral oil hydrocarbons are alkanes and aromatic compounds, it should be possible to assess a degradation potential for these compounds by detection of corresponding catabolic genes. The genes of alkane hydroxylase (AlkB) and catechol 2,3-dioxygenase (C23O) were considered to be useful for this purpose (Sei et al., 2004). The detection of these catabolic genes was initially performed by conventional PCR with published primers (Table 3) (Meyer et al., 1999; Smits et al., 1999). After some optimisation of the PCR conditions using pure cultures, the detection was successful with all positive controls listed in Table 2. The correctness of the PCR products from pure cultures was verified by sequencing. Both, the genes for AlkB and for C23O, could also be amplified from extracts of the investigated soil material. However, no PCR product was obtained with the environmental samples, when forward primer TS2Smod2 for alkB was applied in combination with any of the two reverse primers. To amplify a spectrum of alkane hydroxylase genes as broad as possible an equimolar primer mixture of the forward primers TS2S and TS2Smod was combined with an equimolar mixture of the reverse primers deg1RE and deg1Re2. The amplification of genes for C12O, which was attempted with available primers for gram positive bacteria was successful in the reference strain but not in extracts from the soil material (data not shown). To quantify the amount of degradation genes, the competitive quantitative PCR method was applied, which allowed to follow the development of the
Microbial degradation potential in a bioremediation system 75 degradation potential during the course of bioremediation. In all investigated soil samples more copies of genes for AlkB could be amplified than for C23O. (Fig. 5a, 6a and 7a). As a rule, more copies of the degradation genes could be detected in extracts after the first bead treatment than in extracts from the second/third bead treatment. The heap Rositz 2 showed an increase of the copy number of alkB until the active time period (Rositz 40-45, Rositz 40-45*), then the copy number decreased somewhat, but remained at a higher level than at the beginning of the bioremediation process (Fig 6a). There was only a small variation in copy number of genes for C23O. Copy numbers of the degradation genes for AlkB and C23O were relatively constant in heaps Rositz 1 and Rositz 3 over the whole degradation process (Fig 5a, Fig. 6a). Only marginal variations were detectable. Even the turnover of soil material Rositz 3 did not lead to an appreciable increase of the copy number of catabolic genes. The surprisingly high copy numbers of both degradation genes in sample Rositz 1-10* were probably due to a measuring error of DNA concentration. The copy numbers of genes for AlkB in samples from heaps Grimma and Espenhain (Fig. 7a) were at lower levels than the samples of the three Rositz heaps before start of the technical process. The level of genes for C23O were nearly the same as in the Rositz samples. The copy numbers of the degradation genes varied weakly between the sequential extracts. Although the concentrations of mineral oil hydrocarbons and the structure of the soil material were quite different between the heaps Grimma and Espenhain, the copy numbers of genes for AlkB and C23O, respectively had the same level.
Quantification of the main detected bacterial groups The taxonomic diversity of the bacteria in the active stage of the bioremediation heap Rositz 3 has previously been investigated (Popp et al., in preparation). The detection and quantification of the relevant bacterial groups in heap Rositz 3 during the course of bioremediation was performed by membrane hybridisation with specific probes. Other bioremediation systems were investigated with the same probes. Probes (Table 5) were chosen from literature for the genera Sphingomonas (Neef, 1997), Pseudomonas (Braun-
76 Microbial degradation potential in a bioremediation system
Howland et al., 1993), Acidovorax (Huber, 1997) and Acinetobacter (Kenzaka et al., 1998). The specificity of the probes were checked with the probe match tool of the ARB software (Ludwig et al., 2004). Specific probes for Thiobacillus, Rhodoferax and the clone TRS13 related-Rositz cluster were designed using the ARB software. Signal intensities of environmental samples with specific probes were always very weak and the background signal had a strong influence on the measurement. However, genera, which were detected in a diversity analysis of the active phase (Popp et al., in preparation), could all be detected by membrane hybridisation. In most cases the individually detected genera contributed a higher percentage to the bacterial flora in extracts from the first bead treatment than in extracts from in the second/third bead treatment. This finding applied to all investigated samples from the different bioremediation systems. The genera Sphingomonas and Thiobacillus were detectable in all investigated stages of all five bioremediation systems (Fig. 5b, Fig. 6b, Fig 7b). The signal intensity for some environmental samples was visible but too weak for quantification. This was especially true for signals of probe Ros40-63 (clone TRS13 related-Rositz sequences). Other cases, where signals were detectable, but not suitable for quantification are listed in Table 6.
FIG. 5(a): Copy numbers of the degradation genes for AlkB and C23O in material from the heap Rositz 3 during the course of bioremediation as determined by competitive quantitative PCR. Grey bars: genes for AlkB; black bars: genes for C23O. ■ indicate the temperature of the bioremediation system; ▲ the ambient temperature. Note the logarithmic scale of the axis for gene copies. FIG. 5(b): Quantification of the main detected bacterial groups in material from the heap Rositz 3 during the course of bioremediation by membrane hybridisation of RNA extracts. genus Sphingomonas (S); genus Thiobacillus (T); genus Pseudomonas (P); genus Acidovorax (A); genus Rhodoferax (R); clone TRS13 (C); genus Acinetobacter not detectable. Genera, which were only detectable but not quantifiable at individual time points, are listed in Table 6. (40; S) in sample Rositz 30-34 means that 40 % of the bacterioflora could be detected with the probe for Sphingomonas spp. Rositz 30-34: first bead treatment of a sampling; Rositz 30-34*: second/third bead treatment of the same sampling. Samples were taken at the beginning (Rositz 30-34), during the active degradation (Rositz 46-52; Rositz 64-70; Rositz 71-77; Rositz 78-85) and at the end (Rositz 86- 93) of the process.
Microbial degradation potential in a bioremediation system 77
10000 35,0 turnover of 32,5 soil material
30,0
27,5 1000 25,0
22,5
20,0
100 17,5
15,0 temperature (°C) temperature 12,5
10,0 copies of degradation genes/ng DNA genes/ng degradation of copies 10 7,5
5,0
2,5
1 0,0 Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz 30-34 30-34* 46-52 46-52* 64-70 64-70* 71-77 71-77* 78-85 78-85* 86-93 86-93*
150
140 turnover of soil material
130 35;R
120 16;R
110 19;R 12;A 15;A 100
11;A 90 22;P 80 38;P 7;A 70 37;P 19;T
60 22;P 11;T specific probe/EUB338 (%) probe/EUB338 specific 50 6;T 4;T 40 1;C 30 56;S 48;S 40;S 41;S 10;R 20 0,5;P 5;T 4;A 1;T 15;T 28;S 10 0,6;T 12;S 12;S 14;S 1;T 10;S 10;S 7;S 2;S 0 Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz 30-34 30-34* 46-52 46-52* 64-70 64-70* 71-77 71-77* 78-85 78-85* 86-93 86-93*
78 Microbial degradation potential in a bioremediation system
100000 24 Rositz 1 Rositz 2 22
20 10000 18
16
1000 14
12
10 100 temperature (°C)
8 copies of degradation genes/ng DNA genes/ng of degradation copies 6 10 4
2
1 0 Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz 1-10 1-10* 35-39 35-39* 53-57 53-57* 17-20 17-20* 40-45 40-45* 58-63 58-63*
60 Rositz 1 Rositz 2
55
50 20;R
45 1;C
40 11;R 6;A 17;R 35 6;N 2;T 2;T 0,4;C 0,3;R 30 2;A 1;R 1;P 14;R 2;T 12;R 4;A 25 3;A 5;T 13;R 6;T
specific probe/EUB338 (%) probe/EUB338 specific 20 1;T 7;P 3;A 3;R 1;T 30;S 30;S 0,4;C 15 4;T 5;T 25;S 3;R 22;S 22;S 1;P 10 18;S 18;S 16;S 3;T 16;S 11;S 11;S 5 7;S
0 Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz Rositz 1-10 1-10* 35-39 35-39* 53-57 53-57* 17-20 17-20* 40-45 40-45* 58-63 58-63*
Microbial degradation potential in a bioremediation system 79
FIG. 6(a): Copy numbers of the degradation genes for AlkB and C23O in material from the heaps Rositz 1 and Rositz 2 during the course of bioremediation as determined by competitive quantitative PCR. Grey bars: genes for AlkB; black bars: genes for C23O. ■ indicate the temperature of the bioremediation system; ▲ the ambient temperature. Note the logarithmic scale of the axis for gene copies. FIG. 6(b): Quantification of the main detected bacterial groups in material from the heaps Rositz 1 and Rositz 2 during the course of bioremediation by membrane hybridisation of RNA extracts. genus Sphingomonas (S); genus Thiobacillus (T); genus Pseudomonas (P); genus Acidovorax (A); genus Rhodoferax (R) clone TRS13 (C); genus Acinetobacter (N). Note the different scale of the y-axis in Figure 5 (b). Genera, which were only detectable but not quantifiable at individual time points, are listed in Table 6. (16; S) in sample Rositz 1-10 means that 16 % of the bacterioflora could be detected with the probe for Sphingomonas spp. Rositz 1-10: first bead treatment of a sampling; Rositz 1-10*: second/third bead treatment of the same sampling. Samples were taken at the beginning (Rositz 1-10; Rositz 17-20), during the active degradation (Rositz 35-39; Rositz 40-45) and at the end (Rositz 53-57; Rositz 58-63) of the process.
In bioremediation system Rositz 3 the investigated genera were already at a high level before the start of the technical process and they remained high in active biodegradation material Rositz 46-52 and Rositz 46-52* (Fig. 5b). Especially the genera Sphingomonas, Thiobacillus, Pseudomonas, Acidovorax and Rhodoferax were at a high level in these stages. They made up almost the whole bacterial microflora. In fact the sum exceeded 100%, indicating that strong background signal for probes SPH120 and Ros72-194 probably overestimated the signal intensities. Another possible point could be the overestimated RNA concentration of the environmental samples. A drop of these populations occurred between day 43 and 101 after the start of the process (Table 1, Fig. 5). The genus Acinetobacter was not detectable with probe Ac in any sample. The genera Pseudomonas, Acidovorax and Rhodoferax were not detectable from sample Rositz 64-70* onwards and “clone TRS13” relatives were not detectable from sample Rositz 71-77* onwards. The turnover of soil material resulted only in a small increase of the still detectable genera.
80 Microbial degradation potential in a bioremediation system
1000,0 Grimma Espenhain A
100,0
10,0 copies of degradation genes/ng DN genes/ng degradation of copies
1,0 Grimma 3-11 Grimma 3-11* Espenhain 1-10 Espenhain 1-10*
Grimma Espenhain
100
90 20;R
80
70 17;A
10;R 60 2;A
15;R 50 3;R 3;A 9;A 40 44;P 39;P 16;P specific probe/EUB338 (%) 30 2;T 25;P 20 0,3;T 2;T 4;T 21;S 10 12;S 9;S 7;S
0 Grimma 3-11 Grimma 3-11* Espenhain 1-10 Espenhain 1-10*
FIG. 7(a): Copy numbers of the degradation genes for AlkB and C23O in material from the heaps Grimma and Espenhain at the beginning of the technical bioremediation process as determined by competitive quantitative PCR. Grey bars: genes for AlkB; black bars: genes for C23O. Note the logarithmic scale of the axis for gene copies. FIG. 7(b): Quantification of the main detected bacterial groups in material from the heaps Grimma and Espenhain at the beginning of the technical bioremediation process by membrane hybridisation of RNA extracts. genus Sphingomonas (S); genus Thiobacillus (T); genus Pseudomonas (P); genus Acidovorax (A); genus Rhodoferax (R); clone TRS13 and genus Acinetobacter not detectable. Note the different scale of the y-axis in Figure 5 (b). (9; S) in sample Grimma 3- 11 means that 40 % of the bacterioflora could be detected with the probe for Sphingomonas spp.. Grimma 3-11 and Espenhain 1-10: first bead treatment of a sampling; Grimma 3-11* and Espenhain 1-10*: second/third bead treatment of the same sampling.
Microbial degradation potential in a bioremediation system 81
All investigated bacterial groups were also detected in samples Rositz 1-10 and Rositz 1-10* before the technical start of bioremediation process of heap Rositz
1 (Fig. 6b). Only weak or no signals, however, were obtained with probe PSMg for genus Pseudomonas throughout the process. Acidovorax was only detected in starting material whereas Sphingomonas, Acinetobacter and Rhodoferax were also detected in active biodegradation material. The genera Sphingomonas and Rhodoferax had the highest percentage of the detected bacterial groups.
TABLE 6: Detectable but no quantifiable genera in the individual Rositz samples Heap Sample number Detectable but no quantifiable genera Rositz 3 30-34 clone TRS 13 46-52 clone TRS 13 64-70* clone TRS 13 71-77 clone TRS 13 71-77* Thiobacillus 86-93 Thiobacillus
Rositz 1 1-10 Thiobacillus, Pseudomonas, clone TRS 13 1-10* Pseudomonas, Acinetobacter 35-39 Acinetobacter 35-39* clone TRS 13, Acinetobacter 53-57 Thiobacillus, Pseudomonas, clone TRS 13 53-57* clone TRS 13
Rositz 2 17-20 clone TRS 13 40-45* clone TRS 13 58-63* Pseudomonas, clone TRS 13
Most of the investigated bacterial groups were also detectable in all samples of the bioremediation system Rositz 2 (Fig. 6b). The portion of the dominant bacterial groups Sphingomonas and Rhodoferax was somewhat lower than in the other two Rositz bioremediation systems. In contrast to Rositz 1 and Rositz 3, genus Rhodoferax contributed a higher portion to the extracts after the second/third bead treatments than after the first bead treatment in Rositz 2. The genus Pseudomonas was detected with low intensity mainly in the first bead treatment. Acidovorax was detected only until sample 40-45 and the genus Acinetobacter was not detectable in any sample of bioremediation system Rositz 2.
82 Microbial degradation potential in a bioremediation system
Before starting the process the Grimma material was dominated by the genera Pseudomonas, Rhodoferax and Acidovorax (Fig. 7b). The genera Sphingomonas and Thiobacillus contributed lower portion to the microflora. In contrast, the starting material of bioremediation system Espenhain was dominated by genera Pseudomonas, Sphingomonas and Rhodoferax (Fig. 7b). Genera Thiobacillus and Acidovorax contributed only a small portion of the detected bacterial groups. The genus Acinetobacter and clone TRS13 relatives were not detectable in both bioremediation systems. Despite the different contamination levels and soil material of oil tank storage areas Grimma and Espenhain membrane hybridisation experiments showed nearly the same results. Soil material of heap Grimma could be cleaned within few months whereas soil material of heap Espenhain had to be treated for two years, because of the long lag phases of degradation.
DISCUSSION
Methodical aspects The aim of this study was the detection of a broad range of the catabolic genes alkane hydroxylase (AlkB) and catechol 2,-dioxygenase (C23O), as marker for the degradation potential for mineral oil hydrocarbons. The gene for catechol 1,2-dioxygenases is also involved in the degradation of aromatic compounds (Smith, 1990). Both classes of catechol dioxygenases, however, have different expression levels in phenol and benzoate degrading microcosms, respectively (Sei et al., 2004). In such microcosms C23O genes dominated the degradation under high-load conditions, in which the substrates remained for relatively long periods. Such conditions predominate also in the investigated samples of the present study. The different expression of the catechol dioxygenase classes was also reported for pure cultures (Heald & Jenkins, 1996). Therefore, the investigation focused on the C23O genes for the detection of the degradation potential for aromatic compounds. The genes for alkane hydroxylase were well studied in pure cultures and in the environment (Schwartz & McCoy, 1973; Smits et al., 1999; van Beilen et al., 2003). Alkane hydroxylase systems are widespread and quite divergent in different bacterial genera (van Beilen et al., 2003). Catechol 2,3-dioxygenases are usually subdivided into various subfamilies (Eltis & Bolin, 1996). In contrast
Microbial degradation potential in a bioremediation system 83 to the other subfamilies, subfamily I.2.A is best investigated until now (Junca & Pieper, 2003; Mesarch et al., 2000; Meyer et al., 1999). Furthermore, some investigations showed that many strains contain multiple alkane hydroxylases or catechol 2,3-dioxygenases. A possible reason for the presence of multiple alkane hydroxylases in one strain could be the specificity of each enzyme for a certain range of alkanes (Smits et al., 2002; Whyte et al., 2002b). It is also known that various Pseudomonas strains contain two catechol 2,3- dioxygenases (Chatfield & Williams, 1986) Finding universal primer pairs for the catabolic genes was impossible. For the detection of genes for alkane hydroxylase many primers were described in the literature (Heiss-Blanquet et al., 2005; Smits et al., 1999; Tani et al., 2001). Primers developed by Smits et al. (1999), amplified a broad range of alkane hydroxylases of different genera, mainly Gammaproteobacteria, but also Betaproteobacteria and Actinobacteria. These primers were used for the detection and quantification of genes for alkane hydroxylase in this study. Thus, the known bacterial diversity of the active bioremediation system Rositz 3 was well covered. An exception in this case were Sphingomonas spp. Little is known about the genes for alkane hydroxylases of these genera. The detection of alkB from S. paucimobilis DSM 1098 with a gene probe was successful in only one investigation (Vomberg & Klinner, 2000). The primer pair described by Meyer et al. (1999) detects catechol 2,3-dioxygenase subfamily I.2.A, and particularly C23O genes of Pseudomonas spp. and Sphingomonas spp. These genera dominated the sequence analysis of the active heap Rositz 3 and this primer pair covered the bacterial flora of the soil samples. In a recent investigation, primers for C23O genes for different subfamilies were developed (Hendrickx et al., 2006). But because of the high sequence diversity of AlkB (Vomberg & Klinner, 2000) and the findings of Hendrickx et al. (2006) that universal primers can not designed, it is not possible to detect the whole diversity of the alkB genes and of the C23O genes with one primer pair, respectively. The C12O-primers applied to the nucleic acid extracts in this study were designed for gram positives (Thiel, unpub.). The sequencing analysis of the active bioremediation stage of heap Rositz 3 had shown only a small portion of gram positives (4,5 %). Therefore it is possible, that the PCR experiment failed
84 Microbial degradation potential in a bioremediation system because of this small proportion or the primers did not match well to the sequences of catechol 1,2-dioxygenases in these environmental samples. An established method for the quantification of genes is competitive quantitative PCR. Several studies applied this method for the genes of AlkB and C23O. In a recent study, Heiss-Blanquet et al. (2005) quantified alkane hydroxylases in pristine and contaminated soils. For this Pseudomonas group 1 (substrate specificity: C5 – C12) and the Rhodococcus group (substrate specificity: C6 – 6 8 C30) were investigated. Total gene copies of alkB ranged from 2 x 10 to 3 x 10 per g soil, and the Rhodococcus group dominated. Genes of “Pseudomonas group 1” had a lower value with 104 and 105 copies per gram soil. These lower copy numbers were also obtained in the soil samples investigated here. A reason for this could be the low number of gram positive microorganisms, as suggested by the diversity analysis of heap Rositz 3 (Popp et al., in preparation). Whyte et al. (2002a) performed PCR experiments with different primer pairs and colony hybridisation analyses to detect the diversity of alkB genes in Artic and Antartic hydrocarbon-contaminated and pristine soils. The authors showed with PCR-based methods that Pseudomonas spp. may be enriched in soil samples after the contamination event, whereas Rhodococcus spp. may be predominant in contaminated as well as in pristine soils. The enrichment of Pseudomonas alkB genes could not be shown with colony hybridisation experiments. However, the results of PCR-based analyses were not quantified. The quantification experiments for the C23O genes of Wikström et al. (1996) and Mesarch et al. (2000) are difficult to compare with the findings in the present study. Wikström et al. (1996) reported indeed a drastic decrease of the genes during the degradation process, which was not observed in the Rositz samples, but the amount of degradation genes was not calculated as copy number per ng DNA. Mesarch et al. (2000) seeded catechol 2,3- dioxygenase containing cells of Pseudomonas sp. PpG7 to the soil and therefore overestimated the indigenous degradation potential. The method for quantitative competitive PCR based on DNA level and reflects the degradation potential in the contaminated soil samples. Genes, which are actively involved in the degradation process, are detectable by mRNA. The extraction of mRNA from soils is indeed possible but very tricky at the moment
Microbial degradation potential in a bioremediation system 85
(Bürgmann et al., 2003). Often the yielded amount of extracted mRNA is too low for quantitative investigations. Various genera in the bioremediation systems were quantified by membrane hybridisation with specific probes. RNA were used as probe target, so bacteria with active protein synthesis (Morgan et al., 2002) were detected. The diversity analysis of heap Rositz 3 provided the basis for probe selection. Suitable probes for membrane hybridisation were chosen from the literature in many cases (Table 5) (Braun-Howland et al., 1993; Huber, 1997; Kenzaka et al., 1998; Neef, 1997). Analysable signals were obtained for these probes with the exception of Acinetobacter specific probe Ac. The missing signals for probe Ac could be caused by absence of Acinetobacter spp. in the investigated samples or by mismatching of the probe with the environmental sequences. Because of the sequencing analysis of the active microflora of bioremediation system Rositz 3 the absence of this genus could be excluded (Popp et al., in preparation). A check of the probe with Rositz sequences was not possible because the binding site of the probe was not included into the sequenced range. Another published probe for Acinetobacter spp. (Wagner et al., 1994) would not resolve this problem. Furthermore, the negative control showing 6 mismatches with the probe complicated the identification of sufficiently stringent hybridisation conditions. Some probes were newly designed in this study (Table 5) and were successfully applied to the environmental samples. In this case, the standard curves for the rRNA probes were generated with in vitro transcripts because pure cultures were not available. This method allows to monitor trends in population abundance over time and between various systems (McMahon et al., 1998). The hybridisation behaviour of native rRNA and in vitro transcripts differs somewhat, although the dissociation temperature (Td) is nearly identical, differences in hybridisation signals with transcripts and native rRNA could be explained by specific probe target locations, which are differently denaturated in the two RNAs (McMahon et al., 1998). The quantification of soil samples was limited by a low signal intensity with the specific probes in this investigation. Sometimes cloudy background was the reason for a presumed overestimation of some genera in the environmental samples. Another point is the measurement of the RNA concentration.
86 Microbial degradation potential in a bioremediation system
RiboGreen binds not only to RNA but also to residual traces of DNA (Jones et al., 1998) and possibly to humic substances. So RNA concentration was maybe overestimated. Furthermore, humic substances in the RNA extracts influence also the signal intensity of membrane hybridisation. Because of the binding saturation of membrane a decrease of hybridisation signals can be detected, although probes also bind to a small extent to humic substances (Alm et al., 2000). For high levels of DNA in RNA extracts the influence of the hybridisation signal is the same like for humic substances. Low levels of DNA reduces hybridisation signals (Alm et al., 2000). The probes for sphingomonads, pseudomonads and Acidovorax spp., which were chosen from literature, are mostly group-specific. The probes designed in this study were designed for smaller phylogenetic groups and thus the contribution of these bacterial groups to the microflora can be expected to be very small. However, in comparison with the signals from the group-specific probes the signal from the newly designed probes is relatively high.
Catabolic genes versus bacterial SSU rRNA diversity The investigated degradation genes remained constant during the course of bioremediation process. There was no significant increase at the beginning or decrease at the end of the technical process. However, copy numbers of genes for the alkane hydroxylases were significantly higher than copy numbers of genes for catechol 2,3-dioxygenases. This correlates with alkanes as the dominant contamination (Schmiers, 1973). Furthermore, alkane hydroxylase primers were with 22 positions much more degenerate than C23O-primers with 7 positions. The primers for AlkB detect a larger microbial diversity than the primers for C23O, which might also be a reason for the big difference between the detected copy numbers of the degradation genes. Most of the detected active genera were known for their degradation potential like pseudomonads, sphingomonads and Acinetobacter spp. (Evans et al., 2004; Gallego et al., 2001; Mills et al., 2003; Vomberg & Klinner, 2000). Sequences similar to the Rositz sequences of the genera Rhodoferax (Glöckner et al., 2000; Lüdemann et al., 2000) and Acidovorax (Huber et al., 2003) were often found in aquatic habitats. The occurrence of genus Thiobacillus in contaminated soil was somewhat surprising. This genus is known for
Microbial degradation potential in a bioremediation system 87 autotrophic oxidation of sulphur compounds and not for degradation of mineral oil hydrocarbons, although some species could also grow heterotrophicly (Kelly et al., 2005). No physiological properties are known for bacteria related to clone TRS13 group, because no cultivable representative is available (Marilley & Aragno, 1999; Popp et al., in preparation). Membrane hybridisation experiments detected a higher portion of Sphingomonas spp. than Pseudomonas spp. in the Rositz samples, although sequencing analysis was dominated by pseudomonads. These findings maybe due to PCR and cloning bias. It was previously shown, that the clone distribution is not the same as the species distribution, which is represented by hybridisation experiments (Fuhrmann et al., 1993). In contrast, the Grimma and Espenhain samples were dominated by Pseudomonas spp. Microorganisms detected with probe Ros40-63 targeting clone TRS13 group appear specific to Rositz samples, because no signals were obtained with Grimma and Espenhain samples. Although the degradation potential was constant over the whole technical process, the detected microbial community dropped. Only the genera Sphingomonas and Thiobacillus were detectable in all stages and samples. The membrane hybridisation experiments had shown that the investigated bacteria were at high levels before the start of the technical process and in active biodegradation material (Rositz 35-39, Rositz 40-45, Rositz 46-52) (Table 1, Fig. 5b, Fig. 6b, Fig. 7). Afterwards only some of the investigated genera were detectable. This is especially obvious for bioremediation system Rositz 3. The other Rositz bioremediation systems were a little bit more diverse in the levels of detected bacteria at the end of bioremediation. It is not known which genera supersede the probed groups. Possibly the diversity of a microbial community increased after progressive decontamination. A decrease of the microbial diversity during the biodegradation process was reported by others on several contaminated sites and was mostly connected with an increase of bacterial population responsible for the degradation (Macnaughton et al., 1999; Sei et al., 2004). Such a shift probably took place in the Rositz soil long before starting the technical process. In contrast, soils with lower levels of aged contamination show a greater number and diversity of microorganisms (Fahy et al., 2005). Maybe that is why, more genera could be detected at the end of the technical
88 Microbial degradation potential in a bioremediation system process in the less contaminated heaps Rositz 1 and Rositz 2 than in Rositz 3. Also the sum of the detected genera in most investigated samples from these heaps reached only values smaller than 45 %
Despite of the diversity problem of the primers for the catabolic genes, the detection of these genes probably reflects the activity in a bioremediation system in a better way than a taxonomic analysis. The application of taxonomic probes on environmental samples requires only the identification of the genera of interest. The active microorganisms are indeed detected on RNA level, but no information on their metabolic potential is acquired. However, the constant level of degradation genes during the course of bioremediation process Rositz 3 was surprising. There were also only little differences between a fast degrading heap (Grimma) and a slow degrading heap with a long lag phases (Espenhain). Similarly Heiss-Blanquet et al. (2005) found no obvious differences in gene copy numbers between pristine and contaminated soils. So it is conceivable, that the number of genes are not the limiting factor for a successful degradation, but rather factors like the diffusion of the substrate or an uneven distribution of degrading bacteria. A contributing factor is admittedly that the detection of the catabolic genes in this study as well as in the investigation of Heiss-Blanquet et al. (2005) were performed on DNA level, where also inactive genes were detected. The quantification on mRNA level in soil samples, however, is not well established (Bürgmann et al., 2003).
Concluding remarks Mostly more copies of catabolic genes were detected in the first bead treatment than in the second/third bead treatment. This implies that the main degradation potential was located at the surface of the soil particles and existed already in the material although degradation activity was low on-site. These findings also correlate with the results of the sequencing analysis (Popp et al., in preparation). This taxonomic analysis demonstrated that some genera with known degradation potential, like Sphingomonas spp. and Pseudomonas spp. were mainly extracted in the first bead treatment. The quantification of the catabolic genes showed no correlation with the temperature profile of the Rositz heaps. So the temperature parameter reflects the degradation activity insufficiently.
Microbial degradation potential in a bioremediation system 89
The obtained data for the catabolic genes and for the taxonomic analysis show no real difference between fast degrading systems and slow degrading systems with a long lag phase. Degradation potential for mineral oil hydrocarbons was obviously never limiting the process. Fast degradation was rather a matter of permeability for the ventilation stream.
ACKNOWLEDGEMENTS
We thank Mr. Hagedorn (Dierichs & Hagedorn CONSULTING GmbH Freiberg), Mr. Seelig (Bodensanierungsgesellschaft mbH Seifersbach), and Mr. Schlenker (Bauer und Mourik Umwelttechnik GmbH Roßau) for providing samples and results of their chemical analyses. Nicole Popp is a recipient of a PhD fellowship from the Deutsche Bundesstiftung Umwelt. Michael Schlömann acknowledges generous long term support from the Deutsche Bundesstiftung Umwelt.
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Danksagung
Ich möchte mich bei Professor Michael Schlömann bedanken, dass ich die vorliegende Arbeit in seiner Arbeitsgruppe anfertigen durfte.
Mein besonderer Dank gilt Margit Mau, für die sehr gute Betreuung dieser Arbeit und die große Hilfe beim Projektantrag. Sie hatte stets ein offenes Ohr für meine Fragen und Diskussionspunkte.
Frau Schlegel-Starmann und dem Stipendiatenteam der DBU danke ich für die Finanzierung dieses Projektes, die interessanten Seminare und Veranstaltungen sowie die stete Hilfe bei auftretenden Problemen. Außerdem danke ich Herrn Hagedorn (Dierichs & Hagedorn Consulting GmbH Freiberg), Herrn Schlenker (Baur & Mourik Umwelttechnik GmbH Roßau) sowie Herrn Seelig und Frau Sust (Bodensanierungsanlage mbH Seifersbach) für die gute Zusammenarbeit.
Silke Wegener und Christina König möchte ich danken für die entstandene Freundschaft, die angenehme Arbeitsatmosphäre, die vielen Anregungen für die Arbeit, die Unterstützung während der Schwangerschaft und für die Ermutigungen, wenn es mal wieder nicht so lief wie geplant.
Bei Isabel Weißbach und Susanne Niescher bedanke ich mich für die unzähligen Diskussionen über das Für und Wider der Arbeit.
Sandra Häder, Antje Lohse, Sylvana Tamme und Mandy Wolfram gilt ein großes Dankschön für die schon während des Studiums andauernde Freundschaft, auch wenn die Pflege in letzter Zeit häufig nur noch per E-Mail möglich war.
Der größte Dank gilt meinem lieben René. Er hat mich in meinen Vorhaben und der Erreichung meiner Ziele immer tatkräftig unterstützt, die Tränen getrocknet, wenn es wieder mal keinen Ausweg gab und meine Freude geteilt, wenn alles so funktioniert hat, wie ich es mir vorgestellt hatte.
Meiner kleinen Fabienne danke ich, dass sie mir gezeigt hat, die Welt auch mal wieder von einem anderen Blickwinkel zu sehen und für die vielen schönen gemeinsamen Momente, die mir die Kraft zum Weitermachen gegeben haben. References 109
Ich bedanke mich besonders bei meinen Eltern und Schwiegereltern für die beständige Unterstützung in den letzten Jahren, für die zahlreichen Babysitter- Nachmittage sowie für das große Interesse und Verständnis an meiner Arbeit.
Nicht zuletzt gilt mein Dank meinen Geschwistern Michael und Mathias für ihre übernommenen Onkelpflichten und die Weiterbildung in Stahltechnologie, Feuerfestmaterialen sowie Glas- und Keramikbearbeitung.
Es genügt nicht, den Menschen zu einem Spezialisten zu erziehen. Er wird auf diese Weise lediglich eine Art nützlicher Maschine, nicht aber eine harmonisch entwickelte Persönlichkeit. Das wesentliche Ziel der Erziehung muss sein, dem Studierenden das Verständnis und lebendige Gefühl für die wirklichen Werte des Lebens nahe zu bringen und ihm das Erkennen des Schönen und moralisch Guten zu lehren. Eine Erziehung, die diese Arbeit versäumt, wird in Bezug auf die Vermittlung spezialisierten Wissens Menschen heranbilden, die gut dressierten Hunden gleichen ..... (Albert Einstein)