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Microbial communities involved in the bioremediation of an aged recalcitrant hydrocarbon polluted soil by using organic amendments
Bioresource Technology 101 (2010) 6916–6923
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Microbial communities involved in the bioremediation of an aged recalcitrant hydrocarbon polluted soil by using organic amendments M. Ros *, I. Rodríguez, C. García, T. Hernández Department of Soil and Water Conservation and Organic Waste Management, CEBAS-CSIC, P.O. Box 164, 30100 Espinardo, Murcia, Spain
a r t i c l e
i n f o
Article history: Received 7 December 2009 Received in revised form 22 March 2010 Accepted 29 March 2010 Available online 21 April 2010 Keywords: Sewage sludge Hydrocarbon contaminated soil DGGE Ascomycota Actinobacteria
a b s t r a c t An 8-month field bioremediation experiment using fresh (FS) and composted (CS) sewage sludge and unamended soil (US) was carried out on an aged hydrocarbon contaminated semi-arid soil. FS treatments led to the highest percentage of hydrocarbon degradation (46%) and the highest bacterial and fungal population. Denaturing gradient gel electrophoresis analysis demonstrated differences in bacterial and fungal community structure of treated compared to uncontaminated soil (control). Time of sampling accounted for most of the differences than type of treatment. The principal phyla observed in bioremediation treatments were Actinobacteria and Ascomycota. Results pointed to the addition of organic amendments, particularly sewage sludge, as an useful strategy for improving the effectiveness of landfarming biodegradation processes in hydrocarbon polluted soils. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Soil and water contamination with petroleum-derived hydrocarbons and sludge generated by the petrochemical industry pose difficult environmental problems (Samanta et al., 2002; Gallego et al., 2001). One technique for remediation of oil refinery sludge is landfarming, an attractive in situ bioremediation technology for remote sites that is technically simple and relatively inexpensive (Maila and Cloete, 2004). Landfarming involves the degradation of soil hydrocarbons through the activation of natural microorganisms by the incorporation of inorganic fertilizers, water and periodic tilling to mix and aerate the soil (Harmsen, 1991). Incorporation of organic amendments in the landfarming process has been used to accelerate and increase hydrocarbon degradation because they are a source of microbial biomass and enzymes (Handi et al., 2006; Bastida et al., 2008) and provide nutrients and easily biodegradable substrates to stimulating hydrocarbon degrader microorganisms activity (Adesodum and Mbagwu, 2008). Gallego et al. (2001) reported that the addition of activated sludge from a domestic waste water plant to a natural sandy soil contaminated with diesel (6000 mg kg 1) increased hydrocarbon degradation rates. Wellman et al. (2001) found that the degradation of diesel and motor oil in a loamy soil was higher when 20% manure rather than ammonium sulphate was added. However, some researchers have reported adverse or no beneficial effects of amendments on a mixed coniferous forest and silty sand (Palmroth et al., 2002; * Corresponding author. Tel.: +34 968396388; fax: +34 968396213. E-mail address: [email protected] (M. Ros). 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.03.126
Schaefer and Juliane, 2007). Therefore, a detailed characterization of the contaminated site, environmental conditions and the microbial community are still necessary for in situ bioremediation to become a reliable and safe cleanup technology (Iwamoto and Nasu, 2001). A few long-term studies have been done on soils into which refinery sludges have been periodically incorporated and fresh or composted sewage sludge has been added to enhance soil bioremediation (Handi et al., 2007; Contreras-Ramos et al., 2009). Likewise, little research has been published on the effect of organic amendments on contaminated soil microbial community (Di Gennaro et al., 2009). Molecular methods have demonstrated utility in assessing microbial diversity of various types of environmental samples. Polymerase chain reaction (PCR)-based amplification of the 16S and 18S rRNA genes allows the profiling of complex microbial communities on the basis of sequence diversity, independent of cultivation in the laboratory (Muyzer et al., 1993). Among genetic fingerprinting methods, denaturing gradient gel electrophoresis (DGGE) permits direct visualization of bacterial and fungi diversity in an environmental sample, rapid comparison of samples, and identification of community members by sequence analysis (Roy et al., 2005; Whyte and Greer, 2005; Ros et al., 2006). The aim of this study was to evaluate, the influence of hydrocarbon contamination on soil bacteria and fungi communities under semi-arid conditions; and to determine how the addition of fresh and composted sewage sludge to an aged hydrocarbon contaminated semi-arid soil affects hydrocarbon degradation and microbial community.
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2.3. Enumeration of total bacterial and fungi community in soil samples
2. Methods 2.1. Description of the experimental site and sampling The experimental field was set up in a semi-arid area of SE Spain (Santomera, Murcia; 37.59 N 1.07 W), where oil refinery sludge (Table 1) had been added to soil for more than 10 years. The average annual temperature in this area is 19 °C and total rainfall ranges from 200 to 250 mm per year, mostly between autumn and spring. The clayey subsoil confers a degree of impermeability, which makes the soil suitable for recycling refinery sludge through landfarming. Twelve 85 m2 plots were established in the experimental area and the following treatments were carried out: three plots without organic amendment (US); three plots amended with 180 t ha 1 of fresh sewage sludge (FS); and three plots amended with 180 t ha 1 of sewage sludge compost (CS). The main characteristics of these organic amendments are shown in Table 1. Sewage sludge and compost were spread on the soil surface and mixed with soil by plowing. The experimental plots were aerated by plowing (1–1.5 m depth) at the starting of the experiment and then, monthly for 8 months (landfarming process). Three plots on the adjacent land without hydrocarbon pollution were chosen as control treatments (control). Plots were maintained at 30% of its water holding capacity by spray irrigation to guarantee microbial community activity (Marin et al., 2005). Soil samples (0–20 cm) were collected at the start of the experiment and after 4 and 8 months of bioremediation. Each sample was a homogenized composite of eight subsamples collected along the plot. Samples were placed in sterile plastic bags, sealed and transported to the laboratory, where they were shifted through a 2.0 mm sieve stored at 4 °C for physical and chemical analyses and determination of colony forming units or at 20 °C for molecular analysis. 2.2. Hydrocarbon analysis Total hydrocarbon content was determined by extraction of oil and grease in soxhlet apparatus with chloroform and subsequent treatment with silica gel to remove polar components (APHA, 1995). Total n-alkanes were determined by extraction with hexane:acetone (1:1) in a microwave at maximum power for 10 min. Ten milliliter of the extract were evaporated to dryness in a rotoevaporator and then dissolved in 1 ml of pure hexane. The extracts were purified with alumina cartridges (Waters certified Sep-Pak cartridges). N-alkanes were determined in these concentrated and purified extracts by GC–MS chromatography.
Table 1 Main characteristics of the refinery sludge and the organic amendments.
pH (H2O) EC (1:10) (lS cm 1) TOC (g kg 1) Total N (g kg 1) Total P (g kg 1) Total K (g kg 1) Hydrocarbon content (g kg Pb (mg kg 1) Cd (mg kg 1) Ni (mg kg 1) Zn (mg kg-1) Cu (mg kg 1) Cr (mg kg 1) a
1
)
Refinery sludgea
Fresh sewage sludge
Sewage sludge compost
– – 162.3 – – – 220.7 10 <1.2 24 57 22 21
7.46 2250 350 45 24.8 6.1 – 173 1.8 51 1385 337 132
7.53 2800 360 27 15.9 4.4 – 109 0.7 14 859 270 22
Average values from different refinery sludges.
Colony forming units (CFUs) of culturable fungi and bacteria were determined by plating of serial dilutions of soil samples in sterile quarter-strength Ringer solution on potato dextrose rose agar (PDA 40 g l 1 and Rose Bengal 50 mg l 1) amended with streptomycin 100 mg l 1 and on Tryptic Soy Agar (TSA 4 g l 1, Agar 15 g l 1, Nystatin 50 mg l 1), respectively. The number of fungal and bacterial CFUs per gram of dried soil sample was counted after 3 and 7 d of incubation at 25 °C. 2.4. Physico-chemical and chemical analysis Electrical conductivity (EC) and pH were measured in a 1/5 (w/ v) aqueous solution in a CM2002 (Crison) micro-conductivimeter and pH2002 (Crison) micro pH meter, respectively. Total organic carbon (TOC) was determined by the method of Yeomans and Bremner (1989). Nitrogen was determined by the Kjeldahl method. Total P and total K and heavy metal content were determined in nitric–perchloric (1:1) digestion extract; P was determined by colorimetry following the Murphy and Riley (1962) method and K and heavy metals by atomic absorption spectrometry (Perkin–Elmer 5500). 2.5. Soil DNA extraction, PCR and DGGE analysis Total DNA was extracted from soil samples using the Fast DNA Spin Kit for soil (BIO 101, USA), following the manufacturer’s instructions. The DNA was measured by a Biophotometer (Eppendorf) and subjected to electrophoresis in 1.5% (w/v) agarose gels stained with ethidium bromide and visualized under UV light. Ribosomal RNA gene sequences were amplified in a PCR thermocycler (PCR Thermal Cycler, TAKARA, Dice) with a set of primers purchased from TIB MOLBIOL Syntheselabor Gmbh. FR1-gc/FF390 Vainio and Hantula (2000) and 338f-gc/907r (Muyzer et al., 1993) were employed for amplification of fungal and bacterial sequences, respectively. A GC-clamp was added to the forward primer to improve electrophoretic separation amplicons by DGGE (Muyzer et al., 1993). The PCR reactions were carried out in 25 ll volumes containing a final concentration of 1X PCR buffer, 0.1 mg ml 1 bovine serum albumin (BSA, 5 mg ml 1), 0.2 mM dNTPs mix, 0.2 lM of each primer, 1 U of DNA polymerase (Biotools, B&M Labs, SA), 2 mM MgCl2, 20 mM TMA and 1 ll of 1:10 of extracted DNA. For fungi, the thermocycling program was preceded by an initial denaturation step (94 °C for 8 min) and followed by a final elongation step phase (72 °C for 10 min). Thirtyfive cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 45 s and elongation at 72 °C for 1 min followed. For bacteria, the thermocycling program was preceded by an initial denaturation step (95 °C for 3 min) and a final elongation step (72 °C for 10 min), followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 57 °C for 1 min and elongation at 72 °C for 1 min. Products were checked by electrophoresis in 1.5% (w/v) agarose gels and ethidium bromide staining (10 mg ml 1). PCR products from each sample were analyzed by DGGE using the DCODE™ Universal Mutation detection System (Bio-Rad laboratories, Inc.). For fungi, eight percent polyacrylamide gels [30% acrylamide/bisacrylamide (37.5:1) (Protogel, National diagnostics)] were prepared with a 30% [2.02 M urea, 12% (v/v) formamide] to 60% [4.04 M urea, 24% (v/v) formamide] vertical denaturing gradient gel. For bacteria, seven percent polyacrylamide gels [30% acrylamide/bis-acrylamide (37.5:1) (Protogel, National diagnostics)] were prepared with 40% [2.69 M urea, 16% (v/v) formamide] to 70% [4.71 M urea, 28% (v/v) formamide] vertical denaturing gradient gel. Twenty microliters of PCR products were loaded into the
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denaturing gels. DGGE was performed at 200 V for 10 min, followed by 75 V for 16 h at 60 °C in 1X TAE buffer (40 mM Tris–acetate, 1 mM EDTA). The gels were stained with SYBR Gold (Nucleic acid gel stain) following the manufacturer’s instructions (Invitrogen) and viewed under UV light. DGGE gel images were analysed using Quantity One software (version 4.5, Bio-Rad laboratories, Inc.). The Dice similarity coefficient (Dice, 1945) was determined for the resulting DNA band profiles, and the clustering algorithm of Ward (Ward, 1963) was used to calculate the dendograms of each DGGE considering a band when their intensities were >10–15. Dominant DGGE bands were excised from the gel with a sterile tip into a 1.5 ml tube containing 10 ll of sterile Milli-Q water (24 h). The eluted DNA was reamplified as described above. DGGE was run again to verify the previously observed position of the band. PCR products of the extracted bands were purified by the QIAquick PCR purification kit (Qiagen) and sequenced using a sequencer 3130 (Applied Biosystems). The partial 16S rRNA and 18S rRNA gene sequences were compared with those in GenBank to find the closest sequence matches (Tables 2 and 3) and submitted under accession numbers FJ914589–FJ914616. The percentage of the total number of sequences belonging to a particular class over the to-
tal number of all sequences derived from control or bioremediation treatments were calculated (Table 4). 2.6. Statistical analysis Data were analyzed by SPSS 14.0 software. ANOVA analysis was used for the statistical analysis, with two factors ‘‘time” and ‘‘type of organic amendment”. In case of significant F-statistics Tukey’s post hoc test (P < 0.05) was selected to separate means. 3. Results 3.1. Hydrocarbon degradation In all the bioremediation treatments the total soil hydrocarbon degradation process followed a similar pattern in which two phases could be clearly differentiated: a first rapid phase, lasting 4 months, corresponding to the decomposition of the most labile hydrocarbons, and a second slower phase, during which the more resistant hydrocarbons started to be degraded (Fig. 1a).
Table 2 Sequence analysis of bands excised from DGGE banding pattern from bacteria 16S rRNA. Soil
DGGE band
Closest sequence match (accession number)
(%) Query coverage/(%) similarity
Phylogenetic group
Control 0 Control 8
21
Beta-proteobacterium (EU499550)
57/98
Proteobacteria; Beta-proteobacteria
Control 0 Control 8
22
Rhodocyclales bacterium (EU043549)
100/91
Proteobacteria; Beta-proteobacteria
Control 0 Control 8
23
Arthrobacter sp. SC17Y (AM983505)
81/99
Actinobacteria; Actinomycetales
Control 0 Control 8
24
Arthrobacter sp. AM9T (AM983498)
93/99
Actinobacteria; Actinomycetales
US0–US8
20
Chloroflexi bacterium (AB265898)
100/98
Chloroflexi
CS0–CS8 FS8 US8
19
Mycobacterium brumae (AF480576)
100/100
Actinobacteria; Actinomycetales
CS0–CS8 FS0–FS8 US0–US8
18
Nocardioides sp. CM24C3 (AM936824)
99/87
Actinobacteria; Actinomycetales
CS0 FS0 US0
17
Dietzia sp. ice-oil-71 (DQ533972)
100/99
Actinobacteria; Actinomycetales
CS0 FS0 US0
13
Microbacterium sp. DB-3 (EU439403)
100/99
Actinobacteria; Actinomycetales
CS0–CS8 FS0–FS8 US0–US8
15
Microbacterium arborescens (AM711565)
100/98
Actinobacteria; Actinomycetales
CS8 FS0–FS8 US0–US8
14
Arthrobacter sp. TD3 (EF468655)
99/99
Actinobacteria; Actinomycetales
CS0 FS0 Compost
11
Uncultured bacterium (EU215319)
96/99
Unclassified bacteria
CS0–CS8 FS0–FS8 US0–US8
12
Chromatiales bacterium (AM935143)
100/100
Gamma-proteobacteria
CS0–CS8 FS0–FS8 US0–US8
9
Planomicrobium sp. QD9 (FJ263010)
99/97
Firmicutes
CS0–CS8 Compost
8
Uncultured bacteria (AY332553)
99/97
Unclassified bacteria
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M. Ros et al. / Bioresource Technology 101 (2010) 6916–6923 Table 3 Sequence analysis of bands excised from DGGE banding pattern from fungi 18S rRNA. Soil
DGGE band
Closest sequence match (accession number)
(%) Query coverage/(%) similarity
Phylogenetic group
Control 0 Control 8 CS0–CS8 FS0–FS8 US0–US8 Compost Sewage sludge
1
Fusarium sp. MBS1 (FJ613599)
94/99
Ascomycota
Control 0 Control 8
2
Uncultured fungus (AY275186)
73/95
Unclassified fungi
Control 0 Control 8
3
Alternaria petroselini (AY154685)
91/95
Ascomycota
Control 0 Control 8
4
Uncultured fungus (EU733622)
95/99
Unclassified fungi
CS0–CS8 FS0 US0–US8 Compost Sewage sludge
6
Ochroconis gallopava IFM 52605 (AB125284)
97/99
Ascomycota
FS8 US8
7
Eleutherascus lectardii (DQ062997)
100/99
Ascomycota
FS8
12
Scopulariopsis brevicaulis strain NCPF 2177 (AY083220)
100/99
Ascomycota
CS0–CS8 FS0 Compost
13
Graphium tectonae (U43907)
98/99
Ascomycota
CS0–CS8 FS0 US0–US8 Compost Sewage sludge
9
Eukaryote clone (EF024080)
95/94
Eukaryota
CS0–CS8 FS0 US0 Compost Sewage sludge
14
Fungus clone BAQA64 (AF372718)
98/97
Unclassified fungi
CS0 FS0 US0 Compost Sewage sludge
17
Fungus clone 18S3-47 (EU733622)
98/97
Unclassified fungi
CS0 FS0 US0–US8
11
Soil fungus (EU647859)
93/90
Unclassified fungi
US8
10
Fungus clone T3-IV-3a-21 (EF628893)
97/94
Unclassified fungi
Table 4 Prevalence of different classes of bacteria and fungi in sequenced DGGE bands from control and bioremediation treatments. Bacterial community (%)/treatments
Control
CS
FS
US
Proteobacteria Actinobacteria Firmicutes Others
50 50 – –
10 60 10 20
11.11 66.66 11.11 11.11
11.11 66.66 11.11 11.11
Fungal community
Control
C
SS
US
Ascomycota Unclassified fungi Others
50 50 –
42.86 42.86 14.28
55.55 33.33 11.12
37.50 50 12.5
The highest percentage of total hydrocarbon degradation during the first phase was observed in the FS treatment (45%) followed by CS and US (27% and 22%, respectively) (Fig. 1a). In the second phase the hydrocarbon degradation rate was lower than in the first phase. After 8 months of bioremediation the FS treatment led to the highest percentage of total hydrocarbon degradation (46%), followed by CS (36%) and US (31%) (Fig. 1a).
Profiles of the percentage of total n-alkanes degradation (C10– C36) under the different bioremediation treatments are shown in Fig. 1b. The highest level of degradation of n-alkanes occurred also during the first four months of treatment (Fig. 1b). After 8 months, the degradation of n-alkanes was higher for FS treatment (55%) followed by CS (53%) and US (41%) (Fig. 1b).
3.2. Monitoring of bacteria and fungi colony forming units (CFU) The colony forming units (CFUs) of bacteria and fungi, were significantly (P < 0.05) affected by treatments and sampling time, and also by the interaction between bioremediation treatments and sampling time (Fig. 2). As shown in Fig. 2, bacterial and fungal population of control treatment remained constant along the experiment. The bacterial population of bioremediation treatments increased 2 to 3 orders of magnitude at the beginning of the experiment compared to control soil, while, fungal population increased only 1 order of magnitude. After 8 months of bioremediation both bacteria and fungi were 1 order higher in bioremediation treatments than in control
(a)
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% Total hydrocarbon degradation
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50 US 40 FS 30
CS
20 10 0
(b)
% Total n-alkanes degradation
0
4 Months
8
4 Months
8
60 50 40
US FS CS
30 20 10 0 0
Fig. 1. Percentage of total hydrocarbon degradation in bioremediation treatments (a) percentage of total n-alkanes degradation in bioremediation treatments (b).
Bacteria log10 cfu g-1 dry soil
(a)
11 10 9 8 7 6 5 4 3
US
FS
0
(b)
7.5
US
CS
4 Months
FS
Control
8
CS
Control
Fungi log10 cfu g-1 dry soil
7
Fig. 3. DGGE banding pattern (a) and cluster analysis (Ward method, Dice coefficients of similarity), (b) of 16S rRNA fragments of different treatments. 0 and 8 after treatments (US, FS and CS) represented sampling time. The numbers correspond to bands identified by 16S rRNA gene sequence analysis (Table 2).
6.5 6
3.3. Analysis of different bioremediation treatments by denaturing gradient gel electrophoresis
5.5 5 4.5 4 3.5 3 0
Factor Time (T) Treatment (Treat.) Tx Treat.
4 Months
Bacteria F P 74.79 <0.001 332.62 <0.001 37.47 <0.001
8
Fungi F P 108.85 <0.001 306.54 <0.001 16.81 <0.001
Fig. 2. Bacteria (a) and fungi (b) colony forming units (CFUs) of different treatments.
(Fig. 2). The FS treatment showed significant higher bacterial and fungal population than the rest of the treatments along the experiment, little differences were observed in this regard between compost amended soils (CS) and unamended soils (US).
The effects of different bioremediation treatments on the structure and dynamics of the soil bacterial and fungal community were analyzed by DGGE (Figs. 3a and 4a). The cluster analysis of DGGE banding patterns of bacterial community showed that the control treatments, independently of the sampling time, were grouped far away from the rest of the treatments, suggesting a high difference in bacterial community between hydrocarbon contaminated soils and uncontaminated soils (Fig. 3b). Sewage sludge was grouped separately from sewage sludge amended soils indicating a difference in the bacterial community of this organic material and the sewage sludge amended soils independently of the sampling time (Fig. 3b). Contrarily, compost showed a similar bacterial community to compost amended soil at the beginning of the experiment (CS0) (Fig. 3b). In general, the bacterial communities from different bioremediation treatments changed during the experiment independently of bioremediation treatment (Fig. 3b). The cluster analysis of DGGE banding patterns of fungal community showed that the control treatments had fewer prominent
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(20%), Firmicutes (6.66%), Chloroflexi (6.66%) and unclassified bacteria (13.32%) were identified (Table 2). Control soil independently of the sampling time showed four dominant bands (21, 22, 23 and 24) (Table 2) that were not present in the hydrocarbon contaminated soils. Two of these bands (23, 24) represented Actinobacteria (Actinomycetales), whereas the others represented Proteobacteria (Beta-proteobacteria (21, 22). Five of the fifteen excised bands (9, 12, 14, 15 and 18) were present in samples from all bioremediation treatments independently of the sampling time, and belonged to the phyla Actinobacteria, Firmicutes and Proteobacteria. Bands 8 and 11 represent unclassified bacteria. Band 8 was only observed in compost and compost treated soil, while band 11 was observed in compost and in CS0 and FS0 treatments. Bands 17 and 19 represent Actinomycetales. Band 17 was observed in all treatments at the beginning of bioremediation, but disappeared after 8 months, while band 19 was observed in CS treatments during the entire experiment and in FS and US treatments after 8 months. Band 20 appeared in US treatments and represents Chloroflexi. Actinobacteria and Proteobacteria phyla were present in all cases and Firmicutes were only observed in bioremediation treatments. Actinobacteria were represented to a larger degree in FS and US treatments (66.6%) than in CS (60%) and control treatments (50%), while Proteobacteria sequences were found more frequently in the control (50%) than in bioremediation treatments [CS (10%) and FS and US (11.11%), respectively] (Table 4).
Fig. 4. DGGE banding pattern (a) and cluster analysis (Ward method, Dice coefficients of similarity), (b) of 18S rRNA fragments of different treatments. 0 and 8 after treatments (US, FS and CS) represented sampling time. The numbers correspond to bands identified by 18S rRNA gene sequence analysis (Table 3).
bands than the rest of the treatments and grouped separately, indicating a difference in the fungal communities of uncontaminated and contaminated soils (Fig. 4b). Similarly to bacterial community, sewage sludge showed a different fungal community (Fig. 4b) compared to sewage sludge amended soils whereas compost was grouped with compost treated soils at the beginning of the experiment. Fungal community changed during the experiment independently of the bioremediation treatment. After 8 months of bioremediation the DGGE banding pattern of sewage sludge treatments revealed a change in fungal community (Fig. 4b).
3.4. Phylogenetic and analysis of excised bands 3.4.1. Bacterial population According to Yu and Morrison (2004) the V3–V5 regions of 16S rRNA gene are recommended for DGGE studies and sequence identification. By aligning the retrieved sequences from the dominant bands excised from the bacterial DGGE gels, sequences corresponding to those of Actinobacteria (Actinomycetales) (53.33%), Proteobacteria (Beta-proteobacteria and Gamma-proteobacteria)
3.4.2. Fungi population A total of 13 bands were excised from fungi DGGE gels and the corresponding sequences were represented Ascomycota (46.15%), unclassified fungi (46.15%) and Eukaryota (7.70%) (Table 3). These three groups were observed in all treatments. The relative number of microorganisms belonging to Ascomycota was larger for FS (55.5%) and control (50%) than in CS (42.86%) and US (37.50%) treatments. Sequences from unclassified fungi were more numerous in control and US (50%) respectively, than FS (33.33%) and CS (42.86%); Eukaryota were only present in bioremediation treatments (Table 4). Sequence (bands 1, 2, 3 and 4) from control treatments belonged to Ascomycota and unclassified fungi, and only band 1 was common to all treatments and compost and sewage sludge organic amendments (Fig. 4). Band 12 only appeared in FS treatment and band 7 in FS and US treatments at the end of the bioremediation process (Fig. 4). Both bands belonged to phylum Ascomycota. Band 13 belonged to phylum Ascomycota and appeared in compost and CS treatments during the entire experiment and on SSO treatment. Band 14 representing unclassified fungi, only appeared in CS treatments independently of the sampling time, in SS0 and US0 treatments and also in organic amendments. Band 9 and band 6 representing Eukaryota and Ascomycota respectively, appeared in all bioremediation treatments independently of the sampling time, except in FS treatment where they disappeared during bioremediation process. Both bands were also found in compost and sewage sludge (Fig. 4). Band 10 and 17 belonged to unclassified fungi. Band 10 appeared in US8 and band 17 in all bioremediation treatments at the start of the experiment, but disappeared during bioremediation. It was also found in compost and sewage sludge organic amendments (Fig. 4). Band 11 belonged to unclassified fungi and appeared in US treatment independently of the sampling time, while it appeared in the CS and FS treatments only at the beginning of the experiment. The appearance of one band representing Eukaryote showed that these primers (FR1GC/FF390) are not as specific as Vainio and Hantula (2000) assumed.
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4. Discussion 4.1. Effects of different bioremediation treatments on the degradation of hydrocarbons The reduction of total hydrocarbons may not only be due to biodegradation, but also adsorption to organic compounds and other abiotics factors (Marin et al., 2005). Hydrocarbon degradation is a natural process limited by pH, nutrients, aeration and humidity (Leahy and Colwell, 1990). The higher rate of hydrocarbon degradation observed in FS treatment could be due to its higher content of nutrients such as N and P (Table 1). Similar results were observed by Adesodum and Mbagwu, 2008 and Lee et al. (2008) in hydrocarbon contaminated soils amended with poultry and pig manure compost, respectively. Compost treatment (CS) showed lower hydrocarbon degradation than FS probably due to the hydrocarbon complexation by compost organic matter (Swindoll et al., 1998) and to the lower amount of easily degradable substrates provided with the compost. Some other researches have also reported no beneficial effects of amendments treatments e.g. Rhykerd et al. (1995), Bradock et al. (1997) or Gallego et al. (2001) who postulated that the reason could be due to the salinity produced by an excess of nutrients or a significant amounts of organic compounds present in the organic amendments, where microorganisms prefer readily available additives as nutrient sources over less degradable hydrocarbons. The degradation of n-alkanes was the same as that of the hydrocarbons in all treatments during the first four months. This observation suggests the existence of an active bacterial community in this period (Kaplan and Kitts, 2004; Katsivela et al., 2005), a decrease in hydrocarbons bioavailability (Alexander, 2000), accumulation of toxic hydrocarbon metabolites (Casellas et al., 1997), or enrichment of more recalcitrant compounds (Sabaté et al., 2004). Although the exact nature of the remaining hydrocarbons is not known, they likely consist of branched chain alkanes, multi-ring saturates (naphthenes) and aromatics, each of which may have alkyl side chains (Jorgensen et al., 2000).
4.2. Response of bacterial and fungal community to bioremediation treatments The presence of larger number of appropriate microorganisms and the synergistic effect of fungi and bacteria (Johnsen et al., 2005) is a key to successful bioremediation (Chang and Devinny, 2000). The increases in bacterial and fungal populations observed at the start of bioremediation in amended (FS and CS) and unamended (US) treatments with respect to the control soil could be explained by the fact that the incorporation of aged refinery sludge and organic amendments provided substrates that acted as a source of C and nutrients for microorganisms. As these substrates disappeared during bioremediation due to mineralization of the most easily degradable materials, these microbial communities decreased although they always remained higher than those of the control soil. Wellman et al. (2001) and Rhykerd et al. (1999) indicated that incorporation of organic amendments to hydrocarbon contaminated soils increased soil microbial populations, which is in agreement with the higher bacterial and fungal population observed in the FS treatment with respect to US treatment. However, CS treatment showed levels of microbial populations similar to those of US treatment, which is probably due to lower nitrogen and readily metabolizable sources in the compost (Castellanos and Pratt, 1981; García et al., 1994). Van der Waarde et al. (1995) and Margesin et al. (2000) assumed that changes in microbial numbers was indicative of a stimulated biodegradation process, but did not represent an accurate measurement of the
actual biodegradation and the efficiency of the populations. Also, it is well known that only a small part of soil microorganisms can be isolated and cultivated on laboratory (Tyson and Banfield, 2005). The analysis of DGGE banding patterns reflects the composition of the most dominant microbial community, including the non-culturable members (Gelsomino et al., 1999). The current study revealed that the bacterial and fungal community structure changed under bioremediation treatments compared to control treatment and that the difference was greater with respect to sampling time treatments. The highest phylum observed in bioremediation treatments was Actinobacteria (FS and US (66.6%) and CS (60%)), consisted of a variety of Actinomycetales, including Arthrobacter, Nocardia, Microbacterium and Mycobacterium species, whose capacity to degraded petroleum hydrocarbons are well-known (Margesin et al., 2003; Kloos et al., 2006). The highest fungal division were Ascomycota (FS, 55.5%, CS, 42.86%; US, 37.5%). Viñas et al. (2005) and Liew and Jong (2008) showed similar results in soils contaminated with hydrocarbons and crude oil, respectively. This effect could be due to the incorporation of refinery sludge for more than 10 years that has led to the selection of microbial groups more adapted to live in hydrocarbon contaminated sites (Wu et al., 2008). It is important to emphasize that the fungal community in the FS treatment, showed a different profile at the end of the experiment, where the predominant bands were: band 1 with 99% similarity to Fusarium sp. which according to Viñas et al. (2005) has PAH-degrading capability, and band 12 with 99% similarity to Scopulariopsis brevicaulis, and band 7 with 99% similarity to Eleutherascus lectardi, belonging to Ascomycota. These dominant bands could be associated to the higher degradation of more recalcitrant oil-hydrocarbons observed in FS treatment. Saison et al. (2006) and Innerebner et al. (2005) indicated that the addition of a microbial community in composts does not leave a direct microbial trace in soils, but it has an indirect effect through changes in soil organic matter decomposition. The DGGE analysis showed that compost can have a bacterial community similar to that of compost amended soil at the beginning of the experiment (CS0) (Table 2). Band 8 representing an unclassified bacterium with 97% similarity to bacteria obtained from a straw amended cow manure compost (Green et al., 2004) appeared in both profiles (Fig. 3a, Table 2). The DGGE analysis of fungal community showed some bands that appeared in compost and sewage sludge and in all bioremediation treatments (Bands 1, 6 and 13 closely related to Ascomycota and bands 9, 14 and 17 closely related to unclassified bacteria and Eukaryota), and this observation suggests that these bands could have come from organic amendments and/or also from refinery sludge; however, it will be necessary to conduct a more detailed analysis with other molecular tools e.g. DNA/RNA SIP or cloning to substantiate if compost and sewage sludge leave an imprint on soil microbial communities.
5. Conclusions It can be concluded that high rates of organic amendments encourage hydrocarbon degradation in polluted soils due to the stimulation of microbial population size and activity they produce. This is particularly true when organic amendments with little stabilized organic matter, rich in easily biodegradable C substrates and nutrients (as is the case of fresh sewage sludge), is added. It can also be concluded that although bacteria (Actinobacteria) are the main actors in hydrocarbon degradation, fungal community, particularly Ascomycota members, could play also an important role in degrading recalcitrant oil-hydrocarbons.
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Acknowledgements This work was supported by the Spanish Ministry of Environment by the projects Ref. No. 270/2006/3-1.1 and B05.2/2007/ 2.01. Comments by two anonymous reviewers are gratefully acknowledged. References Adesodum, J.K., Mbagwu, J.S.C., 2008. Distribution of heavy metals and hydrocarbon contents in an alfisol contaminated with waste-lubricating oil amended with organic wastes. Bioresour. Technol. 99, 3195–3204. Alexander, M., 2000. Aging, bioavailability, and overestimation of risk from environmental pollutants. Environ. Sci. Technol. 34, 4259–4265. APHA, 1995. Standard Methods for the Examination of Water and Wastewater, 18th ed. APHA-AWWA-WEF. Bastida, F., Kandeler, E., Moreno, J.L., Ros, M., García, C., Hernández, T., 2008. Application of fresh and composted organic wastes modifies structure, size and activity of soil microbial community under semi-arid climate. Appl. Soil Ecol. 2008, 318–329. Bradock, J.F., Ruth, M.L., Catterall, P.H., Walworth, J.L., McCarthynd, K.A., 1997. Enhancement and inhibition of microbial activity in hydrocarbon-contaminated artic soils: implications for nutrient-amended bioremediation. Environ. Sci. Technol. 31, 2078–2084. Casellas, M., Grifoll, M., Bayona, J.M., Solanas, A.M., 1997. New metabolites in the degradation of fluorene by Arthrobacter sp. strain F101. Appl. Environ. Microbiol. 63, 819–826. Castellanos, J.Z., Pratt, P.F., 1981. Mineralization of manure nitrogen-correlation with laboratory indexes. Soil Sci. Soc. Am. J. 45, 354–357. Chang, S.H., Devinny, J.S., 2000. Bioaugmentation for soil bioremediation. In: Wise, D.L., Trantolo, D.J., Cichon, E.J., Inyang, H.I., Stottmeister, U. (Eds.), Bioremediation of Contaminated Soils. Marcel Dekker, Inc., New York. Contreras-Ramos, S., Álvarez-Bernal, D., Montes-Molina, J.A., Van Cleemput, O., Dendooven, L., 2009. Emission of nitrous oxide from hydrocarbon contaminated soil amended with waste water sludge and earthworms. Appl. Soil Ecol. 41, 69– 76. Dice, L.R., 1945. Measures on the amount of ecologic association between species. Ecology 26, 297–302. Di Gennaro, P., Moreno, B., Annoni, E., Garcia_rodriguez, S., Bestetti, G., Benitez, E., 2009. Dynamic changes in bacterial community structure and in naphthalene dioxygenase expression in vermicompost-amended PAH-contaminated soils. J. Hazard Mater. 172, 1464–1469. Gallego, J.L.R., Loredo, J., Llamas, J.F., Vazquez, F., Sanchez, J., 2001. Bioremediation of diesel-contaminated soils: evaluation of potential in situ techniques by study of bacterial degradation. Biodegradation 12, 325–335. García, C., Hernández, T., Costa, C., 1994. Biochemical parameters in soils generated by the addition of organic wastes. Waste Manage. Res. 12, 457–466. Gelsomino, A., Keijzer-Wolters, C., Cacco, G., van Elsas, J.D., 1999. Assessment of bacterial community structure in soil by polymerase chain reaction and denaturing gradient gel electrophoresis. J. Microbiol. Methods 38, 1–15. Green, S.J., Michel Jr., F.C., Hadar, Y., Minz, D., 2004. Similarity of bacterial communities in sawdust- and straw-amended cow manure composts. FEMS Microbiol. Lett. 233, 115–123. Handi, H., Manusadzˇianas, L., Aoyama, I., Jedidi, N., 2006. Effects of anthracene, pyrene and benzo(a)pyrene spiking and sewage sludge compost amendment on soil ecotoxicity during a bioremediation process. Chemosphere 65, 1153–1162. Handi, H., Benzarti, S., Manusadzˇianas, L., Aoyama, I., Jedidi, N., 2007. Bioaugmentation and biostimulation effects on PAH dissipation and soil ecotoxicity under controlled conditions. Soil Biol. Biochem. 39, 1926–1935. Harmsen, J., 1991. Possibilities and limitations of landfarming for cleaning contaminated soils. In: Olfenbuttel, R.F.H. (Ed.), On Site Bioremediation Process for Xenobiotic and Hydrocarbons Treatment. Butterwort-Hetmann, Stoneham, MA, pp. 255–272. Innerebner, G., Knapp, B., Vasara, T., Romantschuck, M., Insam, H., 2005. Traceability of ammonia-oxidizing bacteria in compost treated soils. Soil Biol. Biochem. 38, 1092–1100. Iwamoto, T., Nasu, M., 2001. Current bioremediation practice and perspective. J. Biosci. Bioeng. 92, 1–8. Johnsen, A.R., Wick, L.Y., Harms, H., 2005. Principles of microbial PAH-degradation in soil. Environ. Pollut. 133, 71–84. Jorgensen, K.S., Puustinen, J., Suortti, A.M., 2000. Bioremediation of petroleum hydrocarbon contaminated soil by composting in biopiles. Environ. Pollut. 107, 245–254. Kaplan, C., Kitts, C.L., 2004. Bacterial succession in a petroleum land treatment unit. Appl. Environ. Microbiol. 70, 1777–1786. Katsivela, E., Moore, E.R.B., Maroukli, D., Strómpl, C., Pieper, D., Kalogerakis, N., 2005. Bacterial community dynamics during in situ bioremediation of petroleum waste sludge in landfarming sites. Biodegradation 16, 169–180. Kloos, K., Munch, J.C., Schloter, M., 2006. A new method for the detection of alkanemonooxygenase homologous genes (alkb) in soils based on PCR-hybridization. J. Microbiol. Methods 66, 486–496.
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Microbial communities involved in the bioremediation of an aged recalcitrant hydrocarbon polluted soil by using organic amendments M. Ros *, I. Rodríguez, C. García, T. Hernández Department of Soil and Water Conservation and Organic Waste Management, CEBAS-CSIC, P.O. Box 164, 30100 Espinardo, Murcia, Spain
a r t i c l e
i n f o
Article history: Received 7 December 2009 Received in revised form 22 March 2010 Accepted 29 March 2010 Available online 21 April 2010 Keywords: Sewage sludge Hydrocarbon contaminated soil DGGE Ascomycota Actinobacteria
a b s t r a c t An 8-month field bioremediation experiment using fresh (FS) and composted (CS) sewage sludge and unamended soil (US) was carried out on an aged hydrocarbon contaminated semi-arid soil. FS treatments led to the highest percentage of hydrocarbon degradation (46%) and the highest bacterial and fungal population. Denaturing gradient gel electrophoresis analysis demonstrated differences in bacterial and fungal community structure of treated compared to uncontaminated soil (control). Time of sampling accounted for most of the differences than type of treatment. The principal phyla observed in bioremediation treatments were Actinobacteria and Ascomycota. Results pointed to the addition of organic amendments, particularly sewage sludge, as an useful strategy for improving the effectiveness of landfarming biodegradation processes in hydrocarbon polluted soils. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Soil and water contamination with petroleum-derived hydrocarbons and sludge generated by the petrochemical industry pose difficult environmental problems (Samanta et al., 2002; Gallego et al., 2001). One technique for remediation of oil refinery sludge is landfarming, an attractive in situ bioremediation technology for remote sites that is technically simple and relatively inexpensive (Maila and Cloete, 2004). Landfarming involves the degradation of soil hydrocarbons through the activation of natural microorganisms by the incorporation of inorganic fertilizers, water and periodic tilling to mix and aerate the soil (Harmsen, 1991). Incorporation of organic amendments in the landfarming process has been used to accelerate and increase hydrocarbon degradation because they are a source of microbial biomass and enzymes (Handi et al., 2006; Bastida et al., 2008) and provide nutrients and easily biodegradable substrates to stimulating hydrocarbon degrader microorganisms activity (Adesodum and Mbagwu, 2008). Gallego et al. (2001) reported that the addition of activated sludge from a domestic waste water plant to a natural sandy soil contaminated with diesel (6000 mg kg 1) increased hydrocarbon degradation rates. Wellman et al. (2001) found that the degradation of diesel and motor oil in a loamy soil was higher when 20% manure rather than ammonium sulphate was added. However, some researchers have reported adverse or no beneficial effects of amendments on a mixed coniferous forest and silty sand (Palmroth et al., 2002; * Corresponding author. Tel.: +34 968396388; fax: +34 968396213. E-mail address: [email protected] (M. Ros). 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.03.126
Schaefer and Juliane, 2007). Therefore, a detailed characterization of the contaminated site, environmental conditions and the microbial community are still necessary for in situ bioremediation to become a reliable and safe cleanup technology (Iwamoto and Nasu, 2001). A few long-term studies have been done on soils into which refinery sludges have been periodically incorporated and fresh or composted sewage sludge has been added to enhance soil bioremediation (Handi et al., 2007; Contreras-Ramos et al., 2009). Likewise, little research has been published on the effect of organic amendments on contaminated soil microbial community (Di Gennaro et al., 2009). Molecular methods have demonstrated utility in assessing microbial diversity of various types of environmental samples. Polymerase chain reaction (PCR)-based amplification of the 16S and 18S rRNA genes allows the profiling of complex microbial communities on the basis of sequence diversity, independent of cultivation in the laboratory (Muyzer et al., 1993). Among genetic fingerprinting methods, denaturing gradient gel electrophoresis (DGGE) permits direct visualization of bacterial and fungi diversity in an environmental sample, rapid comparison of samples, and identification of community members by sequence analysis (Roy et al., 2005; Whyte and Greer, 2005; Ros et al., 2006). The aim of this study was to evaluate, the influence of hydrocarbon contamination on soil bacteria and fungi communities under semi-arid conditions; and to determine how the addition of fresh and composted sewage sludge to an aged hydrocarbon contaminated semi-arid soil affects hydrocarbon degradation and microbial community.
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2.3. Enumeration of total bacterial and fungi community in soil samples
2. Methods 2.1. Description of the experimental site and sampling The experimental field was set up in a semi-arid area of SE Spain (Santomera, Murcia; 37.59 N 1.07 W), where oil refinery sludge (Table 1) had been added to soil for more than 10 years. The average annual temperature in this area is 19 °C and total rainfall ranges from 200 to 250 mm per year, mostly between autumn and spring. The clayey subsoil confers a degree of impermeability, which makes the soil suitable for recycling refinery sludge through landfarming. Twelve 85 m2 plots were established in the experimental area and the following treatments were carried out: three plots without organic amendment (US); three plots amended with 180 t ha 1 of fresh sewage sludge (FS); and three plots amended with 180 t ha 1 of sewage sludge compost (CS). The main characteristics of these organic amendments are shown in Table 1. Sewage sludge and compost were spread on the soil surface and mixed with soil by plowing. The experimental plots were aerated by plowing (1–1.5 m depth) at the starting of the experiment and then, monthly for 8 months (landfarming process). Three plots on the adjacent land without hydrocarbon pollution were chosen as control treatments (control). Plots were maintained at 30% of its water holding capacity by spray irrigation to guarantee microbial community activity (Marin et al., 2005). Soil samples (0–20 cm) were collected at the start of the experiment and after 4 and 8 months of bioremediation. Each sample was a homogenized composite of eight subsamples collected along the plot. Samples were placed in sterile plastic bags, sealed and transported to the laboratory, where they were shifted through a 2.0 mm sieve stored at 4 °C for physical and chemical analyses and determination of colony forming units or at 20 °C for molecular analysis. 2.2. Hydrocarbon analysis Total hydrocarbon content was determined by extraction of oil and grease in soxhlet apparatus with chloroform and subsequent treatment with silica gel to remove polar components (APHA, 1995). Total n-alkanes were determined by extraction with hexane:acetone (1:1) in a microwave at maximum power for 10 min. Ten milliliter of the extract were evaporated to dryness in a rotoevaporator and then dissolved in 1 ml of pure hexane. The extracts were purified with alumina cartridges (Waters certified Sep-Pak cartridges). N-alkanes were determined in these concentrated and purified extracts by GC–MS chromatography.
Table 1 Main characteristics of the refinery sludge and the organic amendments.
pH (H2O) EC (1:10) (lS cm 1) TOC (g kg 1) Total N (g kg 1) Total P (g kg 1) Total K (g kg 1) Hydrocarbon content (g kg Pb (mg kg 1) Cd (mg kg 1) Ni (mg kg 1) Zn (mg kg-1) Cu (mg kg 1) Cr (mg kg 1) a
1
)
Refinery sludgea
Fresh sewage sludge
Sewage sludge compost
– – 162.3 – – – 220.7 10 <1.2 24 57 22 21
7.46 2250 350 45 24.8 6.1 – 173 1.8 51 1385 337 132
7.53 2800 360 27 15.9 4.4 – 109 0.7 14 859 270 22
Average values from different refinery sludges.
Colony forming units (CFUs) of culturable fungi and bacteria were determined by plating of serial dilutions of soil samples in sterile quarter-strength Ringer solution on potato dextrose rose agar (PDA 40 g l 1 and Rose Bengal 50 mg l 1) amended with streptomycin 100 mg l 1 and on Tryptic Soy Agar (TSA 4 g l 1, Agar 15 g l 1, Nystatin 50 mg l 1), respectively. The number of fungal and bacterial CFUs per gram of dried soil sample was counted after 3 and 7 d of incubation at 25 °C. 2.4. Physico-chemical and chemical analysis Electrical conductivity (EC) and pH were measured in a 1/5 (w/ v) aqueous solution in a CM2002 (Crison) micro-conductivimeter and pH2002 (Crison) micro pH meter, respectively. Total organic carbon (TOC) was determined by the method of Yeomans and Bremner (1989). Nitrogen was determined by the Kjeldahl method. Total P and total K and heavy metal content were determined in nitric–perchloric (1:1) digestion extract; P was determined by colorimetry following the Murphy and Riley (1962) method and K and heavy metals by atomic absorption spectrometry (Perkin–Elmer 5500). 2.5. Soil DNA extraction, PCR and DGGE analysis Total DNA was extracted from soil samples using the Fast DNA Spin Kit for soil (BIO 101, USA), following the manufacturer’s instructions. The DNA was measured by a Biophotometer (Eppendorf) and subjected to electrophoresis in 1.5% (w/v) agarose gels stained with ethidium bromide and visualized under UV light. Ribosomal RNA gene sequences were amplified in a PCR thermocycler (PCR Thermal Cycler, TAKARA, Dice) with a set of primers purchased from TIB MOLBIOL Syntheselabor Gmbh. FR1-gc/FF390 Vainio and Hantula (2000) and 338f-gc/907r (Muyzer et al., 1993) were employed for amplification of fungal and bacterial sequences, respectively. A GC-clamp was added to the forward primer to improve electrophoretic separation amplicons by DGGE (Muyzer et al., 1993). The PCR reactions were carried out in 25 ll volumes containing a final concentration of 1X PCR buffer, 0.1 mg ml 1 bovine serum albumin (BSA, 5 mg ml 1), 0.2 mM dNTPs mix, 0.2 lM of each primer, 1 U of DNA polymerase (Biotools, B&M Labs, SA), 2 mM MgCl2, 20 mM TMA and 1 ll of 1:10 of extracted DNA. For fungi, the thermocycling program was preceded by an initial denaturation step (94 °C for 8 min) and followed by a final elongation step phase (72 °C for 10 min). Thirtyfive cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 45 s and elongation at 72 °C for 1 min followed. For bacteria, the thermocycling program was preceded by an initial denaturation step (95 °C for 3 min) and a final elongation step (72 °C for 10 min), followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 57 °C for 1 min and elongation at 72 °C for 1 min. Products were checked by electrophoresis in 1.5% (w/v) agarose gels and ethidium bromide staining (10 mg ml 1). PCR products from each sample were analyzed by DGGE using the DCODE™ Universal Mutation detection System (Bio-Rad laboratories, Inc.). For fungi, eight percent polyacrylamide gels [30% acrylamide/bisacrylamide (37.5:1) (Protogel, National diagnostics)] were prepared with a 30% [2.02 M urea, 12% (v/v) formamide] to 60% [4.04 M urea, 24% (v/v) formamide] vertical denaturing gradient gel. For bacteria, seven percent polyacrylamide gels [30% acrylamide/bis-acrylamide (37.5:1) (Protogel, National diagnostics)] were prepared with 40% [2.69 M urea, 16% (v/v) formamide] to 70% [4.71 M urea, 28% (v/v) formamide] vertical denaturing gradient gel. Twenty microliters of PCR products were loaded into the
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denaturing gels. DGGE was performed at 200 V for 10 min, followed by 75 V for 16 h at 60 °C in 1X TAE buffer (40 mM Tris–acetate, 1 mM EDTA). The gels were stained with SYBR Gold (Nucleic acid gel stain) following the manufacturer’s instructions (Invitrogen) and viewed under UV light. DGGE gel images were analysed using Quantity One software (version 4.5, Bio-Rad laboratories, Inc.). The Dice similarity coefficient (Dice, 1945) was determined for the resulting DNA band profiles, and the clustering algorithm of Ward (Ward, 1963) was used to calculate the dendograms of each DGGE considering a band when their intensities were >10–15. Dominant DGGE bands were excised from the gel with a sterile tip into a 1.5 ml tube containing 10 ll of sterile Milli-Q water (24 h). The eluted DNA was reamplified as described above. DGGE was run again to verify the previously observed position of the band. PCR products of the extracted bands were purified by the QIAquick PCR purification kit (Qiagen) and sequenced using a sequencer 3130 (Applied Biosystems). The partial 16S rRNA and 18S rRNA gene sequences were compared with those in GenBank to find the closest sequence matches (Tables 2 and 3) and submitted under accession numbers FJ914589–FJ914616. The percentage of the total number of sequences belonging to a particular class over the to-
tal number of all sequences derived from control or bioremediation treatments were calculated (Table 4). 2.6. Statistical analysis Data were analyzed by SPSS 14.0 software. ANOVA analysis was used for the statistical analysis, with two factors ‘‘time” and ‘‘type of organic amendment”. In case of significant F-statistics Tukey’s post hoc test (P < 0.05) was selected to separate means. 3. Results 3.1. Hydrocarbon degradation In all the bioremediation treatments the total soil hydrocarbon degradation process followed a similar pattern in which two phases could be clearly differentiated: a first rapid phase, lasting 4 months, corresponding to the decomposition of the most labile hydrocarbons, and a second slower phase, during which the more resistant hydrocarbons started to be degraded (Fig. 1a).
Table 2 Sequence analysis of bands excised from DGGE banding pattern from bacteria 16S rRNA. Soil
DGGE band
Closest sequence match (accession number)
(%) Query coverage/(%) similarity
Phylogenetic group
Control 0 Control 8
21
Beta-proteobacterium (EU499550)
57/98
Proteobacteria; Beta-proteobacteria
Control 0 Control 8
22
Rhodocyclales bacterium (EU043549)
100/91
Proteobacteria; Beta-proteobacteria
Control 0 Control 8
23
Arthrobacter sp. SC17Y (AM983505)
81/99
Actinobacteria; Actinomycetales
Control 0 Control 8
24
Arthrobacter sp. AM9T (AM983498)
93/99
Actinobacteria; Actinomycetales
US0–US8
20
Chloroflexi bacterium (AB265898)
100/98
Chloroflexi
CS0–CS8 FS8 US8
19
Mycobacterium brumae (AF480576)
100/100
Actinobacteria; Actinomycetales
CS0–CS8 FS0–FS8 US0–US8
18
Nocardioides sp. CM24C3 (AM936824)
99/87
Actinobacteria; Actinomycetales
CS0 FS0 US0
17
Dietzia sp. ice-oil-71 (DQ533972)
100/99
Actinobacteria; Actinomycetales
CS0 FS0 US0
13
Microbacterium sp. DB-3 (EU439403)
100/99
Actinobacteria; Actinomycetales
CS0–CS8 FS0–FS8 US0–US8
15
Microbacterium arborescens (AM711565)
100/98
Actinobacteria; Actinomycetales
CS8 FS0–FS8 US0–US8
14
Arthrobacter sp. TD3 (EF468655)
99/99
Actinobacteria; Actinomycetales
CS0 FS0 Compost
11
Uncultured bacterium (EU215319)
96/99
Unclassified bacteria
CS0–CS8 FS0–FS8 US0–US8
12
Chromatiales bacterium (AM935143)
100/100
Gamma-proteobacteria
CS0–CS8 FS0–FS8 US0–US8
9
Planomicrobium sp. QD9 (FJ263010)
99/97
Firmicutes
CS0–CS8 Compost
8
Uncultured bacteria (AY332553)
99/97
Unclassified bacteria
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M. Ros et al. / Bioresource Technology 101 (2010) 6916–6923 Table 3 Sequence analysis of bands excised from DGGE banding pattern from fungi 18S rRNA. Soil
DGGE band
Closest sequence match (accession number)
(%) Query coverage/(%) similarity
Phylogenetic group
Control 0 Control 8 CS0–CS8 FS0–FS8 US0–US8 Compost Sewage sludge
1
Fusarium sp. MBS1 (FJ613599)
94/99
Ascomycota
Control 0 Control 8
2
Uncultured fungus (AY275186)
73/95
Unclassified fungi
Control 0 Control 8
3
Alternaria petroselini (AY154685)
91/95
Ascomycota
Control 0 Control 8
4
Uncultured fungus (EU733622)
95/99
Unclassified fungi
CS0–CS8 FS0 US0–US8 Compost Sewage sludge
6
Ochroconis gallopava IFM 52605 (AB125284)
97/99
Ascomycota
FS8 US8
7
Eleutherascus lectardii (DQ062997)
100/99
Ascomycota
FS8
12
Scopulariopsis brevicaulis strain NCPF 2177 (AY083220)
100/99
Ascomycota
CS0–CS8 FS0 Compost
13
Graphium tectonae (U43907)
98/99
Ascomycota
CS0–CS8 FS0 US0–US8 Compost Sewage sludge
9
Eukaryote clone (EF024080)
95/94
Eukaryota
CS0–CS8 FS0 US0 Compost Sewage sludge
14
Fungus clone BAQA64 (AF372718)
98/97
Unclassified fungi
CS0 FS0 US0 Compost Sewage sludge
17
Fungus clone 18S3-47 (EU733622)
98/97
Unclassified fungi
CS0 FS0 US0–US8
11
Soil fungus (EU647859)
93/90
Unclassified fungi
US8
10
Fungus clone T3-IV-3a-21 (EF628893)
97/94
Unclassified fungi
Table 4 Prevalence of different classes of bacteria and fungi in sequenced DGGE bands from control and bioremediation treatments. Bacterial community (%)/treatments
Control
CS
FS
US
Proteobacteria Actinobacteria Firmicutes Others
50 50 – –
10 60 10 20
11.11 66.66 11.11 11.11
11.11 66.66 11.11 11.11
Fungal community
Control
C
SS
US
Ascomycota Unclassified fungi Others
50 50 –
42.86 42.86 14.28
55.55 33.33 11.12
37.50 50 12.5
The highest percentage of total hydrocarbon degradation during the first phase was observed in the FS treatment (45%) followed by CS and US (27% and 22%, respectively) (Fig. 1a). In the second phase the hydrocarbon degradation rate was lower than in the first phase. After 8 months of bioremediation the FS treatment led to the highest percentage of total hydrocarbon degradation (46%), followed by CS (36%) and US (31%) (Fig. 1a).
Profiles of the percentage of total n-alkanes degradation (C10– C36) under the different bioremediation treatments are shown in Fig. 1b. The highest level of degradation of n-alkanes occurred also during the first four months of treatment (Fig. 1b). After 8 months, the degradation of n-alkanes was higher for FS treatment (55%) followed by CS (53%) and US (41%) (Fig. 1b).
3.2. Monitoring of bacteria and fungi colony forming units (CFU) The colony forming units (CFUs) of bacteria and fungi, were significantly (P < 0.05) affected by treatments and sampling time, and also by the interaction between bioremediation treatments and sampling time (Fig. 2). As shown in Fig. 2, bacterial and fungal population of control treatment remained constant along the experiment. The bacterial population of bioremediation treatments increased 2 to 3 orders of magnitude at the beginning of the experiment compared to control soil, while, fungal population increased only 1 order of magnitude. After 8 months of bioremediation both bacteria and fungi were 1 order higher in bioremediation treatments than in control
(a)
M. Ros et al. / Bioresource Technology 101 (2010) 6916–6923
% Total hydrocarbon degradation
6920
50 US 40 FS 30
CS
20 10 0
(b)
% Total n-alkanes degradation
0
4 Months
8
4 Months
8
60 50 40
US FS CS
30 20 10 0 0
Fig. 1. Percentage of total hydrocarbon degradation in bioremediation treatments (a) percentage of total n-alkanes degradation in bioremediation treatments (b).
Bacteria log10 cfu g-1 dry soil
(a)
11 10 9 8 7 6 5 4 3
US
FS
0
(b)
7.5
US
CS
4 Months
FS
Control
8
CS
Control
Fungi log10 cfu g-1 dry soil
7
Fig. 3. DGGE banding pattern (a) and cluster analysis (Ward method, Dice coefficients of similarity), (b) of 16S rRNA fragments of different treatments. 0 and 8 after treatments (US, FS and CS) represented sampling time. The numbers correspond to bands identified by 16S rRNA gene sequence analysis (Table 2).
6.5 6
3.3. Analysis of different bioremediation treatments by denaturing gradient gel electrophoresis
5.5 5 4.5 4 3.5 3 0
Factor Time (T) Treatment (Treat.) Tx Treat.
4 Months
Bacteria F P 74.79 <0.001 332.62 <0.001 37.47 <0.001
8
Fungi F P 108.85 <0.001 306.54 <0.001 16.81 <0.001
Fig. 2. Bacteria (a) and fungi (b) colony forming units (CFUs) of different treatments.
(Fig. 2). The FS treatment showed significant higher bacterial and fungal population than the rest of the treatments along the experiment, little differences were observed in this regard between compost amended soils (CS) and unamended soils (US).
The effects of different bioremediation treatments on the structure and dynamics of the soil bacterial and fungal community were analyzed by DGGE (Figs. 3a and 4a). The cluster analysis of DGGE banding patterns of bacterial community showed that the control treatments, independently of the sampling time, were grouped far away from the rest of the treatments, suggesting a high difference in bacterial community between hydrocarbon contaminated soils and uncontaminated soils (Fig. 3b). Sewage sludge was grouped separately from sewage sludge amended soils indicating a difference in the bacterial community of this organic material and the sewage sludge amended soils independently of the sampling time (Fig. 3b). Contrarily, compost showed a similar bacterial community to compost amended soil at the beginning of the experiment (CS0) (Fig. 3b). In general, the bacterial communities from different bioremediation treatments changed during the experiment independently of bioremediation treatment (Fig. 3b). The cluster analysis of DGGE banding patterns of fungal community showed that the control treatments had fewer prominent
M. Ros et al. / Bioresource Technology 101 (2010) 6916–6923
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(20%), Firmicutes (6.66%), Chloroflexi (6.66%) and unclassified bacteria (13.32%) were identified (Table 2). Control soil independently of the sampling time showed four dominant bands (21, 22, 23 and 24) (Table 2) that were not present in the hydrocarbon contaminated soils. Two of these bands (23, 24) represented Actinobacteria (Actinomycetales), whereas the others represented Proteobacteria (Beta-proteobacteria (21, 22). Five of the fifteen excised bands (9, 12, 14, 15 and 18) were present in samples from all bioremediation treatments independently of the sampling time, and belonged to the phyla Actinobacteria, Firmicutes and Proteobacteria. Bands 8 and 11 represent unclassified bacteria. Band 8 was only observed in compost and compost treated soil, while band 11 was observed in compost and in CS0 and FS0 treatments. Bands 17 and 19 represent Actinomycetales. Band 17 was observed in all treatments at the beginning of bioremediation, but disappeared after 8 months, while band 19 was observed in CS treatments during the entire experiment and in FS and US treatments after 8 months. Band 20 appeared in US treatments and represents Chloroflexi. Actinobacteria and Proteobacteria phyla were present in all cases and Firmicutes were only observed in bioremediation treatments. Actinobacteria were represented to a larger degree in FS and US treatments (66.6%) than in CS (60%) and control treatments (50%), while Proteobacteria sequences were found more frequently in the control (50%) than in bioremediation treatments [CS (10%) and FS and US (11.11%), respectively] (Table 4).
Fig. 4. DGGE banding pattern (a) and cluster analysis (Ward method, Dice coefficients of similarity), (b) of 18S rRNA fragments of different treatments. 0 and 8 after treatments (US, FS and CS) represented sampling time. The numbers correspond to bands identified by 18S rRNA gene sequence analysis (Table 3).
bands than the rest of the treatments and grouped separately, indicating a difference in the fungal communities of uncontaminated and contaminated soils (Fig. 4b). Similarly to bacterial community, sewage sludge showed a different fungal community (Fig. 4b) compared to sewage sludge amended soils whereas compost was grouped with compost treated soils at the beginning of the experiment. Fungal community changed during the experiment independently of the bioremediation treatment. After 8 months of bioremediation the DGGE banding pattern of sewage sludge treatments revealed a change in fungal community (Fig. 4b).
3.4. Phylogenetic and analysis of excised bands 3.4.1. Bacterial population According to Yu and Morrison (2004) the V3–V5 regions of 16S rRNA gene are recommended for DGGE studies and sequence identification. By aligning the retrieved sequences from the dominant bands excised from the bacterial DGGE gels, sequences corresponding to those of Actinobacteria (Actinomycetales) (53.33%), Proteobacteria (Beta-proteobacteria and Gamma-proteobacteria)
3.4.2. Fungi population A total of 13 bands were excised from fungi DGGE gels and the corresponding sequences were represented Ascomycota (46.15%), unclassified fungi (46.15%) and Eukaryota (7.70%) (Table 3). These three groups were observed in all treatments. The relative number of microorganisms belonging to Ascomycota was larger for FS (55.5%) and control (50%) than in CS (42.86%) and US (37.50%) treatments. Sequences from unclassified fungi were more numerous in control and US (50%) respectively, than FS (33.33%) and CS (42.86%); Eukaryota were only present in bioremediation treatments (Table 4). Sequence (bands 1, 2, 3 and 4) from control treatments belonged to Ascomycota and unclassified fungi, and only band 1 was common to all treatments and compost and sewage sludge organic amendments (Fig. 4). Band 12 only appeared in FS treatment and band 7 in FS and US treatments at the end of the bioremediation process (Fig. 4). Both bands belonged to phylum Ascomycota. Band 13 belonged to phylum Ascomycota and appeared in compost and CS treatments during the entire experiment and on SSO treatment. Band 14 representing unclassified fungi, only appeared in CS treatments independently of the sampling time, in SS0 and US0 treatments and also in organic amendments. Band 9 and band 6 representing Eukaryota and Ascomycota respectively, appeared in all bioremediation treatments independently of the sampling time, except in FS treatment where they disappeared during bioremediation process. Both bands were also found in compost and sewage sludge (Fig. 4). Band 10 and 17 belonged to unclassified fungi. Band 10 appeared in US8 and band 17 in all bioremediation treatments at the start of the experiment, but disappeared during bioremediation. It was also found in compost and sewage sludge organic amendments (Fig. 4). Band 11 belonged to unclassified fungi and appeared in US treatment independently of the sampling time, while it appeared in the CS and FS treatments only at the beginning of the experiment. The appearance of one band representing Eukaryote showed that these primers (FR1GC/FF390) are not as specific as Vainio and Hantula (2000) assumed.
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4. Discussion 4.1. Effects of different bioremediation treatments on the degradation of hydrocarbons The reduction of total hydrocarbons may not only be due to biodegradation, but also adsorption to organic compounds and other abiotics factors (Marin et al., 2005). Hydrocarbon degradation is a natural process limited by pH, nutrients, aeration and humidity (Leahy and Colwell, 1990). The higher rate of hydrocarbon degradation observed in FS treatment could be due to its higher content of nutrients such as N and P (Table 1). Similar results were observed by Adesodum and Mbagwu, 2008 and Lee et al. (2008) in hydrocarbon contaminated soils amended with poultry and pig manure compost, respectively. Compost treatment (CS) showed lower hydrocarbon degradation than FS probably due to the hydrocarbon complexation by compost organic matter (Swindoll et al., 1998) and to the lower amount of easily degradable substrates provided with the compost. Some other researches have also reported no beneficial effects of amendments treatments e.g. Rhykerd et al. (1995), Bradock et al. (1997) or Gallego et al. (2001) who postulated that the reason could be due to the salinity produced by an excess of nutrients or a significant amounts of organic compounds present in the organic amendments, where microorganisms prefer readily available additives as nutrient sources over less degradable hydrocarbons. The degradation of n-alkanes was the same as that of the hydrocarbons in all treatments during the first four months. This observation suggests the existence of an active bacterial community in this period (Kaplan and Kitts, 2004; Katsivela et al., 2005), a decrease in hydrocarbons bioavailability (Alexander, 2000), accumulation of toxic hydrocarbon metabolites (Casellas et al., 1997), or enrichment of more recalcitrant compounds (Sabaté et al., 2004). Although the exact nature of the remaining hydrocarbons is not known, they likely consist of branched chain alkanes, multi-ring saturates (naphthenes) and aromatics, each of which may have alkyl side chains (Jorgensen et al., 2000).
4.2. Response of bacterial and fungal community to bioremediation treatments The presence of larger number of appropriate microorganisms and the synergistic effect of fungi and bacteria (Johnsen et al., 2005) is a key to successful bioremediation (Chang and Devinny, 2000). The increases in bacterial and fungal populations observed at the start of bioremediation in amended (FS and CS) and unamended (US) treatments with respect to the control soil could be explained by the fact that the incorporation of aged refinery sludge and organic amendments provided substrates that acted as a source of C and nutrients for microorganisms. As these substrates disappeared during bioremediation due to mineralization of the most easily degradable materials, these microbial communities decreased although they always remained higher than those of the control soil. Wellman et al. (2001) and Rhykerd et al. (1999) indicated that incorporation of organic amendments to hydrocarbon contaminated soils increased soil microbial populations, which is in agreement with the higher bacterial and fungal population observed in the FS treatment with respect to US treatment. However, CS treatment showed levels of microbial populations similar to those of US treatment, which is probably due to lower nitrogen and readily metabolizable sources in the compost (Castellanos and Pratt, 1981; García et al., 1994). Van der Waarde et al. (1995) and Margesin et al. (2000) assumed that changes in microbial numbers was indicative of a stimulated biodegradation process, but did not represent an accurate measurement of the
actual biodegradation and the efficiency of the populations. Also, it is well known that only a small part of soil microorganisms can be isolated and cultivated on laboratory (Tyson and Banfield, 2005). The analysis of DGGE banding patterns reflects the composition of the most dominant microbial community, including the non-culturable members (Gelsomino et al., 1999). The current study revealed that the bacterial and fungal community structure changed under bioremediation treatments compared to control treatment and that the difference was greater with respect to sampling time treatments. The highest phylum observed in bioremediation treatments was Actinobacteria (FS and US (66.6%) and CS (60%)), consisted of a variety of Actinomycetales, including Arthrobacter, Nocardia, Microbacterium and Mycobacterium species, whose capacity to degraded petroleum hydrocarbons are well-known (Margesin et al., 2003; Kloos et al., 2006). The highest fungal division were Ascomycota (FS, 55.5%, CS, 42.86%; US, 37.5%). Viñas et al. (2005) and Liew and Jong (2008) showed similar results in soils contaminated with hydrocarbons and crude oil, respectively. This effect could be due to the incorporation of refinery sludge for more than 10 years that has led to the selection of microbial groups more adapted to live in hydrocarbon contaminated sites (Wu et al., 2008). It is important to emphasize that the fungal community in the FS treatment, showed a different profile at the end of the experiment, where the predominant bands were: band 1 with 99% similarity to Fusarium sp. which according to Viñas et al. (2005) has PAH-degrading capability, and band 12 with 99% similarity to Scopulariopsis brevicaulis, and band 7 with 99% similarity to Eleutherascus lectardi, belonging to Ascomycota. These dominant bands could be associated to the higher degradation of more recalcitrant oil-hydrocarbons observed in FS treatment. Saison et al. (2006) and Innerebner et al. (2005) indicated that the addition of a microbial community in composts does not leave a direct microbial trace in soils, but it has an indirect effect through changes in soil organic matter decomposition. The DGGE analysis showed that compost can have a bacterial community similar to that of compost amended soil at the beginning of the experiment (CS0) (Table 2). Band 8 representing an unclassified bacterium with 97% similarity to bacteria obtained from a straw amended cow manure compost (Green et al., 2004) appeared in both profiles (Fig. 3a, Table 2). The DGGE analysis of fungal community showed some bands that appeared in compost and sewage sludge and in all bioremediation treatments (Bands 1, 6 and 13 closely related to Ascomycota and bands 9, 14 and 17 closely related to unclassified bacteria and Eukaryota), and this observation suggests that these bands could have come from organic amendments and/or also from refinery sludge; however, it will be necessary to conduct a more detailed analysis with other molecular tools e.g. DNA/RNA SIP or cloning to substantiate if compost and sewage sludge leave an imprint on soil microbial communities.
5. Conclusions It can be concluded that high rates of organic amendments encourage hydrocarbon degradation in polluted soils due to the stimulation of microbial population size and activity they produce. This is particularly true when organic amendments with little stabilized organic matter, rich in easily biodegradable C substrates and nutrients (as is the case of fresh sewage sludge), is added. It can also be concluded that although bacteria (Actinobacteria) are the main actors in hydrocarbon degradation, fungal community, particularly Ascomycota members, could play also an important role in degrading recalcitrant oil-hydrocarbons.
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