Response of Microbial Community to Petroleum Stress and Phosphate Dosage in Sediments of Jiaozhou Bay, China

ZHAO Yangguo, CHEN Min BAI Jie,, LI Xinwei Farhana Zulfiqar and WANG Qianli

1) College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, P. R. China

2) Key Laboratory of Marine Environment and Ecology, Ministry of Education, Qingdao 266100, P. R. China

Response of Microbial Community to Petroleum Stress and Phosphate Dosage in Sediments of Jiaozhou Bay, China

ZHAO Yangguo1),2), CHEN Min1), BAI Jie1),2),*, LI Xinwei1), Farhana Zulfiqar1), and WANG Qianli1)

1) College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, P. R. China

2) Key Laboratory of Marine Environment and Ecology, Ministry of Education, Qingdao 266100, P. R. China

The dynamic microcosms were used to evaluate the effect of oil spills on microbial ecological system in marine sediment and the enhancement of nutrient on the oil removal. The function and structure of microbial community caused by the oil pollution and phosphate dosage were simultaneously monitored by dehydrogenase activity assay and PCR-denaturing gradient gel electrophoresis (DGGE) techniques. The results indicated that the amount of total bacteria in all dynamic microcosms declined rapidly with incubation time. The number of petroleum-degrading bacteria and the activity of sediment dehydrogenase were gradually enhanced by petroleum in the oil-treated microcosms, while they both showed no obvious response to phosphate dosage. In comparison, phosphate spiked heterotrophic bacteria and they showed a significant increase in amount. DGGE profiles indicated that petroleum dosage greatly changed community structure, and the bacteria belonged to class Deltaproteobacteria, and phyla Bacteroidetes and Chlorobi were enriched. This study demonstrated that petroleum input greatly impacted the microbial community structure and consequently the marine sediment petroleum-degrading activity was enhanced. Phosphate dosage would multiply heterotrophic bacteria but not significantly enhance the petroleum degradation.

marine sediments; oil spills; dehydrogenase activity; phosphate; heterotrophic bacteria; bacterial community

1 Introduction

In the process of marine petroleum exploitation, oil spill or discharge frequently occurs, and this has caused serious pollution for marine environment. As one of global environmental pollutants, petroleum hydrocarbon presents a great threat to the environmental ecology and human health owing to its wide distribution, high stability, high rate of biological enrichment and carcinogenicity (Atlas, 1995; Li et al., 2010). The problems of marine oil pollution have been drawing the great attention of environmental engineers and ecological scientists in recent years (Kurtz, 2004; Powell et al., 2007). Therefore, the prevention, control and evaluation of oil environmental pollution have been constantly studied (Dutta and Harayama, 2000; Powell et al., 2007; Kostka et al., 2011).

Large quantity of oil spill is one of the reasons for deterioration of marine environment. Research shows that the oil would stay in the marine environment for a long time so as to cause great harm to marine ecological environment and lead to serious economic loss for aquaculture, tourism and other industries (Sauer et al., 1998). When the oil enters the ocean, it will blend with new water body or sediment through the process of volatilization, dissolution, biodegradation, and adsorption (Dutta and Harayama, 2000). While microbial degradation in the sediments might be an important way to minimize oil pollutants, due to the oil-polluted seawater often containing a higher concentration of nitrogen nutrient and a relative lower content of phosphorus, thus this would lead to the phosphorus restriction and prevent further biodegradation (Atlas, 1981). Some reports show that deficiency of nitrogen or phosphorus is the main limited factor for biodegradation of marine oil pollutants, and petroleum-degrading bacteria would grow well when mass ratio of N/P is 4:1 (Atlas, 1981).

Jiaozhou Bay is a semi-enclosed bay connected to the Yellow Sea off China’s coast. The eastern and northeastern coasts of the bay are industrial concentrated area of the Qingdao city. The Huangdao development zones, on the south of the bay, harbor a national crude oil reserve base with the capability of stockpiling more than 3 million tons of crude oil, and a large oil refining project with a designed processing capacity of 10 million tons per year(Dang et al., 2010). The concentrations of oil-derived aliphatic (n-alkanes) and polyaromatic hydrocarbons (PAHs) rang from 0.5 to 8.2 μg g−1dry weight (DW) and 0.02 to 2.2μg g−1DW, respectively, in coastal sediments (Cao et al., 2011). At present, although there are a few investigation reports on petroleum hydrocarbon pollution in Jiaozhou Bay (Wang et al., 2006; Cao et al., 2011), the microbial community structure and function under the condition of oil spill still need to be further explored.

Using continuous-flow microcosms to simulate the submarine environment, this study was designed to explore the response of microbes in sediments to the oil pollutants by examining the dehydrogenase activity (DHA), changes of bacterial numbers and the dynamics of bacterial community structure. During this process, phosphate was dosed to spike the activity of microorganisms in petroleum removal. The results of this research would provide scientific basis for studying microbial degradation of petroleum pollutants and evaluating petroleum pollution.

Table 1 The properties of sea water at sampling station

2 Materials and Methods

2.1 Sampling

A sediment sampler (KC, Denmark) was used to collect sediment samples from the layer of 0 to −40 cm from estuary of Licun River in Jiaozhou Bay (120.3125°E, 36.1564°N) on September 29, 2008. Sediment was transported immediately to the laboratory for experiment. The properties of sea water and sediment are shown in the Table 1 and Table 2.

2.2 Set-up and Operation of the Dynamic Microcosms

Three parallel microcosms were established to reveal the response of sediment to petroleum hydrocarbons, the characteristics of petroleum degradation and the corresponding changes of microbial community structure when the phosphate was added to the polluted sediment. The cylindrical microcosms were made of organic glass with an effective volume of 1.0 L. They all contained the input and output holes and periodic stirring equipment. A total of 0.3 L of sediment sample was put in each microcosm and the seawater was also added. Peristaltic pump was applied to achieve periodically dynamic influent. Microcosm 1 was the control and its influent was seawater. The influent of microcosm 2 contained emulsified petroleum (Liaohe oilfield, China) with a final concentration of 1000 mg L−1. For the microcosm 3, 26 μmol L−1of Na2HPO4·12H2O was added into the influent together with 1000 mg L−1of emulsified petroleum to enhance the degrading efficiency of petroleum-degrading bacteria.

The seawater (or emulsified oil) was injected into the microcosms from their bottom by peristaltic pumps and the effluent flowed out from their top. Following the variation of the ocean tide, the microcosms were treated with the recycles of 24 h. The cycles included one hour of stirring following one hour of pause. Then the peristaltic pumps began to work with the influent speed 0.5 mL min−1. Eight hours later, the pumps stopped working and the blender stirred for one hour, and then followed one hour of pause. After that, the peristaltic pumps started to work for 12 h. Sediments were collected from three microcosms every 5 d. Part of fresh samples were sealed in the plastic bags and used for bacteria counting and dehydrogenase examining. The rest were put into a 10 mL of centrifugal tube and stored at −20℃ until the DNA extraction.

2.3 The Oil Removal Rate

According to the method described by Tang et al. (2012), the residual oil in the effluent was examined by UV Spectrophotometry. The oil removal rate was calculated with the formula Ro= (C0−Ci)/C0, where Ro is oil removal rate, C0and Ciare the oil concentration in influent and effluent, respectively.

2.4 Dehydrogenase Activity and Bacterial Count

As in the description of Gong et al. (1997), DHA assay was based on the reduction of 2, 3, 5-triphenyltetrazolium chloride (TTC) to the creaming red-colored formazan (TPF). The enzyme quantity that bacteria suspension produced 1 µg TPF in 1 h was defined as one enzyme activity unit (µg TPF g−1).

The cells in the diluted samples were fixed by adding formaldehyde with a ratio of 9:1 in volume. 4, 6-diamino-2-phenylindole (DAPI) was used to stain the cell in the mixture as in the method of Kemp et al. (1993). Epifluorescence microscopy (Omla, Leica, Germany) was applied to detect the total fluorescent microbes under 365 nm of wave length and bacterial density in the original sample was calculated with the formula described by Jr. Kepner and Pratt (1994). Plate count method was employed to determine the number of heterotrophic bacteria(Randall and Hemmingsen, 1994). Briefly, sediment samples were serially diluted with 0.9% saline. Then 100 µL of suspension was transferred onto 2216E agar plates. After 3 to 5 d of inoculation at 30℃, the numbers of heterotrophic bacteria were counted. An amended Bushnell-Haas culture media (containing 0.1% diesel fuel) was used to count the petroleum-degrading bacteria by the most-probable number (MPN) method as that in the descriptions of Taylor and Viraraghavan (1999).

In order to determine whether the phosphate addition had stimulated the petroleum degradation or not, the values obtained from dehydrogenase activity and bacterial enumeration were analyzed using ANOVA (Dhanpal et al., 2009).

2.5 Microbial Community Structure

Bacterial community structure analysis was performed by PCR-denaturing gradient gel electrophoresis (DGGE) based on partial 16S rRNA gene. According to the provided instructions, DNA was extracted from sediment samples by a soil DNA extraction kit (Mobio, USA). The extracted DNA was served as a template and PCR amplification was conducted by using the 16S rRNA gene universal primers BA341/BA534 (5 ’end with a GC clamp in BA534 primer) (Muyzer et al., 1993) in a 9700 PCR equipment (ABI, USA). Primers were bought from Nanjing GenScript Biotech Co., Ltd, China. PCR and DGGE analysis was conducted in accordance with the method described by Muyzer et al. (1993).

Shannon-Wiener diversity index (H') was calculated for each lane of DGGE profiles according to the formula H′=−∑Pi lnPi, where Pi is the relative signal strength of each band in the lane. Cluster analysis for microbial community in each lane was carried out by Ward’s method with SPSS software (SPSS Inc., the Chicago IL).

Dominant bands in DGGE profiles were recovered and reamplified with the method mentioned above. Sequences for the positive clones were determined according to the descriptions of Zhao et al. (2010). The partial 16S rRNA gene sequences were classified with Classifier program in the RDP (http://rdp.cme.msu.edu/) and then the Blast program at NCBI (http://www.ncbi.nlm.nih.gov/) was used to retrieve the most similar sequences.

2.6 Accession Number of DNA Sequences in GenBank

In this study, the bacterial 16S rRNA gene sequences had been submitted to GenBank with accession numbers JQ316496-JQ316508.

Table 3 Changes of oil removal rate in sediments

3 Results and Discussion

3.1 Oil Removal by the Sediments

The oil in the effluent was quantitatively detected and the removal rate was summarized in Table 3. In the initial stage, the oil in effluent was 10.6 and 14.8 mg L−1, respectively, in the petroleum treatment and the petroleum plus phosphate treatment. However, we detected as high as 5 mg g−1DW of oil in the sediment at this time (data now shown), and a layer of crude oil was also observed on the inner wall of microcosms. Therefore, the high removal rate at the beginning might be ascribed to sediment adsorption and oil viscosity, and the removal rate could not reflect the real degradation ability of the oil degrading bacteria. Some other reports have the similar results (Cao et al., 2011).

3.2 Changes of Dehydrogenase Activity

Dehydrogenases are essential components of the enzyme systems of microorganisms; they play a major role in producing energy and triggering the petroleum degradation. Higher values reflect the greater ability of petroleum-degrading microbes and the rapid degradation efficiency (Tang et al., 2012). DHA can therefore be used as an indicator of biological redox systems and as a measure to evaluate the ability of microorganism decomposing pollutants (Xu and Obbard, 2003; Tang et al., 2012). To gain a better understanding of the active status of microorganisms, the TTC-dehydrogenase activity was adopted to indicate the DHA of the sediment in the three models during the culturing process. The results are shown in Fig.1.

Fig.1 The dynamics of dehydrogenase activity in the control, petroleum and petroleum plus phosphate microcosms.

The initial value of the DHA in the three models was 882 µg TPF g−1DW (Fig.1). After three months, it changed to 342, 40250, and 42630 µg TPF g−1DW, respectively, in control, petroleum treated and petroleum plus phosphate treated microcosms. It is clear that the DHA in the control (Microcosm 1) was always at a very low level, but it was significantly higher in the petroleum-treated sedi-ment. During the primary stage, the DHA presented similar changing tendency in the two petroleum-polluted microcosms (Microcosms 2 and 3) and increased gradually in both microcosms. However, the total bacteria and heterotrophic bacteria (Figs.2a and 2b) did not increase at this moment. It might be that most of bacteria could not adapt themselves to the high oil concentration and were gradually be washed out. By comparison, some petroleum-degrading bacteria then containing more dehydrogenase existed and some increased in number (Fig.2c). During the experiment, the dehydrogenase in oil-polluted microcosm with phosphate and the petroleum-polluted microcosm presented similar activity (P>0.05). It was therefore that phosphate dosage could not obviously improve the activity of petroleum-degrading bacteria. This is ascribed to the fact that different kinds of marine bacteria have different enzyme systems and different requirements for N, P nutrients (Xu and Obbard, 2003).

Fig.2 Changes of total bacteria (a), heterotrophic bacteria (b) and petroleum-degrading bacteria (c) in the control, petroleum and petroleum plus Phosphate microcosms.

3.3 Effect of Petroleum on the Bacterial Quantity

According to Fig.2c, petroleum-degrading bacteria increased with the petroleum addition, while they presented little response to the phosphate dosage (P>0.05). 5 d later, the number of petroleum-degrading bacteria in petroleum-treated microcosms reached 2.3 × 104MPN g−1DW, and approached 15.0 × 104MPN g−1DW on the 45th day. These results correlated well with the changing tendency of DHA (Fig.1). However, the response of petroleumdegrading bacteria quantity obviously lagged behind that of DHA. It was due to that the petroleum-degrading bacteria required more time to reproduce and enrich, while DHA quickly increased and responded to the petroleum addition. Garcia et al. (1997) observed a similar behavior between the indices of microbial activity (basal respiration and biomass carbon) and the DHA values, and this confirms that DHA can be used as a sensitive marker of microbial activity.

The number of heterotrophic bacteria in the petroleum plus phosphate microcosm (Microcosm 3) was higher (4.4 × 105CFU (Colony-Forming Units) g−1DW, in average) than that in the other two microcosms (the living bacteria in Microcosm 1 was 1.3 × 105CFU g−1DW, and that in Microcosm 2 was about 1.2×105CFU g−1DW) (Fig.2b). At the beginning of experiment, the number of heterotrophic bacteria in oil-polluted model was less than that in unpolluted models. It infers that high concentration of oil caused temporarily suppress on microorganisms. With the decrease of oil pollution concentration and microbes’ adaptation to the environment, the bacteria utilized petroleum hydrocarbons and multiplied. As a result, the number of petroleum-degrading bacteria began to increase and gradually exceeded that from the control. Fig.2B shows that the average number of heterotrophic bacteria in the sediment with petroleum and phosphate was larger than that in sediment without phosphate (P<0.05). It indicates that the addition of phosphate benefited heterotrophic bacterial growth and reproduction. This accords well with the previous theory that the available nitrogen and phosphorus limit the growth and activities of microorganisms in marine environment (Atlas, 1981). However, according to Fig.1 and Fig.2c, phosphate in sediment had little effect on DHA and petroleum-degrading bacteria. It is inferred that phosphate promoted some heterotrophic bacterial growth and reproduction, but did not significantly affected the activity of dehydrogenase and hydrocarbon-degrading bacteria. Thus, it was clear that the total number of heterotrophic bacteria has no direct relationship with petroleum degradation, and petroleum-degrading bacteria formed only a very small part of the heterotrophic bacteria. Many reports (Xu and Obbard, 2003; Head et al., 2006) had documented that there were few microorganisms that could utilize directly oil as carbon source in sediment, but the microorganisms enriched gradually under the long-term stress of oil pollutants once the sediment was polluted by oil and finally increased in number. In Fig.2b, we found that the number of heterotrophic bacteria in sediment increased at first and then decreased on the 50th day. This might explain that intermediate product was generated during the degradation of oil and its toxicity was greater than that of petroleum contaminant itself (Head et al., 2006).

Fig.2 shows that the number of heterotrophic bacteria was by three orders of magnitude lower than the total cell counts, so the quantity changes of heterotrophic bacteria had slight effect on the total. At the beginning, the totalnumber of bacteria in the sediment was 1.08×109cell g−1DW (Fig.2a). 5 d later, the number of bacteria in all dynamic microcosm models reduced quickly. At this point, microorganisms entered the models soon and they were all in an adaptation process or they were rushed out by the strong effluent. Although the total bacteria in the three models had some variations, their averages were close (approximately 5.0×108cell g−1DW).

3.4 The Response of Bacterial Community Structure to Petroleum

Based on the above experimental results, sediment samples at key points were designated for the analysis of microbial community structure. Sediment samples were selected on the first, 60th and 100th day. DGGE profiles are shown in Fig.3A and the cluster analysis in Fig.3B.

Fig.3 Bacterial DGGE profiles based on partial 16S rRNA genes (A) and Cluster analysis for DGGE profiling samples (B). S, the original sediment; C, the sediment treated with seawater; O, the sediment treated with oil; and Op, the sediment treated with oil plus phosphate.

The results show that the DGGE profiles of seven different samples, there existed marked differences in number and density. The bands B1, B2, B9 and B11 were unique for the sample O-60 d (sediment treated with oil for 60 d). The bands B5 and B13 were unique for the sample S (original sediment), suggesting that the microbes of the two bands were native. Band B4 was found only in samples S, C-60 d (sediment treated with seawater for 60 d), C-100d and Op-100 d (sediments treated with oil plus phosphate for 100 d). We deduced that this species was not adapted to high oil levels but could survive under low oil condition. Band B10 merely appeared in the samples S, C-60 d and Op-60 d. Band B3 appeared in all seven samples, indicating that this species was a native and could acclimatize to oil environment. From the above description, it is clear that the predominant bacterial populations existing in the seven samples were different. In addition, the densities of bands in different sediment samples were different. For example, band B4 was a dominant band in samples S and C-60 d, but was weak in C-100 d and Op-100 d, and even not detected in other samples. Therefore, the differences of major bacterial abundance among the different samples were obvious. The band number of DGGE profiles from bacterial V3 region corresponds to the quantity of the dominant bacterial species in the samples, while bands with different density at the same location reflect the quantitative difference of the dominant bacteria. From an overall perspective, there were less dominant bacteria in samples O-60 d and C-100 d, but, on the contrary, sample O-100 d contained most bacteria. The DGGE fingerprinting of S and C-60 d appeared most similar on the basis of cluster analysis, so they clustered into one group. The rest of the community formed a large group, but O-60 d and Op-60 d were closer.

As summarized in Table 4, Shannon index of sediment samples was calculated in accordance with the band density in DGGE profiles. After the cultivation of microcosms, all the sediment samples presented a higher diversity than the initial sediment. Statistics showed that the diversity of bacteria in the soil samples was significantly negatively correlated to petroleum hydrocarbons content of every point (Kostka et al., 2011).

Each band of DGGE profiles roughly corresponds to one of the predominant bacterial species in the community (Muyzer et al., 1993). Typical bands B1-B13 in Fig.3A were designated to be cloned and sequenced. The similar 16S rRNA gene sequences are listed in Table 5. According to the band sequencing results, the most similar species of the majority were uncultured microorganisms. This suggests that the sediment of Jiaozhou Bay perceived a large number of microbial resources. Of all the sequences, 84.6% belonged to Proteobacteria, in which 53.8% belonged to the Deltaproteobacteria and 30.8% belonged to the Gammaproteobacteria. Many reports show that Proteobacteria members are the main part of petroleum-degraders. Reddy et al. (2011) showed that Proteobacteria is dominant representing 50% of the totalpopulation after the removal of total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs) from the real-field petroleum sludge. By using the direct pyrosequencing technique, dos Santos et al. (2011) also found that the Proteobacteria, in particular the classes Gammaproteobacteria and Deltaproteobacteria, are prevalent before and after the simulated oil spill in the pristine mangrove sediment. Similarly, Paissé et al. (2008) detected high dominance of bacteria related to the Proteobacteria phylum in chronically oil-polluted retention basin sediment located in the Berre lagoon. In the oil-spilled beach sands of Gulf of Mexico, the members of the Gammaproteobacteria and Alphaproteobacteria are the key players in oil degradation (Kostka et al., 2011).

Table 4 The diversity of the bacterial communities in the sediments from the DGGE profiles

Table 5 The similar sequence of bands from bacterial PCR-DGGE profiles

The most similar sequence of B4 was Halomonas variabilis strain DSM 3051 (the similarity was 100%). This bacterium was isolated from the Great Salt Lake in the United States and was a species of family Halomonadaceae in class Gammaproteobacteria. It could grow in a variety of saline environment, including Salt Lake, marine and other salt water environment as a consequence of its powerful vitality. Oie et al. (2007) found that Halomonas sp. could degrade benzene derivatives in the environment of high salt and pH. Garcia et al. (2004) pointed out that the moderately halophilic bacterium Halomonas sp. could effectively degrade aromatic compounds. After adding the oil, band B4 still existed, and it was presumed that its bacterium could bear some quantity of petroleum. The similar sequence of band B10 was Desulfocella sp. DSM 2056 (99%). This species belongs to the family Desulfobacteraceae and it is found in marine mud and is a kind of completely oxidized sulfate-reducing bacterium (SRB) (Meyer and Kuever, 2007). Its main feature is the sulfate reduction, using sulfate as the electron acceptor to degrade organic pollutants (Zhao et al., 2010). Band B10 was a dominant band in the microbial community in petroleum-contaminated sediment samples, suggesting that B10 was a dominant bacteria or it was likely to be an unreported oil degrading bacteria, and it might be related to desulfurization of petroleum hydrocarbons in seawater environment. Suárez-Suárez et al. (2011) showed that crude oil contaminants enhance the sulfate reduction rates, and that autochthonous deltaproteobacterial SRBs are able to degrade crude oil or polycyclic aromatic hydrocarbons. The similarity of B11 and Winogradskyella sp. MOLA 379 was 99%. This bacterial strain belongs to family Flavobacteriaceae and has been found in South Pacific seawater. Chikere et al. (2009) showed that the members of class Flavobacterium in phylum Bacteroidetes are a ubiquitous marine oil degrading bacteria. Band B11 existed in oil-contaminated sediment and it had highest density in O (100 d). This indicates that it could at least endure petroleum hydrocarbons and is even related to biodegradation of petroleum hydrocarbons.

It shows that the dominant bacteria are the members of phyla Proteobacteria, in particular, Delta- and Gammaproteobacteria in our petroleum-contained sediment microcosms. This suggests that they might play the key roles in the petroleum removal.

4 Conclusions

Enzyme activity assays and molecular fingerprint technology were applied to investigate microbial function and community structure in sediments under the influence of the oil and phosphate by dynamic microcosms. Conclusions are drawn as follows:

The phosphate addition had little influence on totalbacteria and the petroleum-degrading bacteria, but spiked the growth and reproduction of heterotrophic bacteria.

Petroleum improved dehydrogenase activity significantly, but phosphate dosage had little impact on dehydrogenase activity.

The dominant bacteria of DGGE profiles belonged to phyla Proteobacteria, Bacteroidetes and Chlorobi. Adding phosphate did not significantly change the dominant populations.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 40801193), the Basic Research Projects of Qingdao Science and Technology Program (12-1-4-1-(12)-jch) and the Scientific Research Foundation for the Excellent Middle-Aged and Youth Scientists of Shandong Province of China (BS2011NJ018).

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(Edited by Ji Dechun)

(Received November 2, 2012; revised April 9, 2013; accepted May 8, 2013)

© Ocean University of China, Science Press and Springer-Verlag Berlin Heidelberg 2014

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E-mail: baijie@ouc.edu.cn