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Bioremediation Using Highly Specific Microbial Consortia for Petroleum Hydrocarbons (PHCS) Degradation

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08/02/20 Sciences Reference this

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Bioremediation using highly specific microbial consortia for petroleum hydrocarbons (PHCs) degradation in contaminated soils

Highlights

  • Cell enumeration was used to compare the abundance of cells before and after the treatments of soil.
  • Gas chromatography-mass spectrometry was used to calculate total petroleum hydrocarbon using Squalane as an internal standard to normalise data.
  • Bioinformatics was used to taxonomically classify microbial consortia based on their DNA fragments extracted using a PowerSoil® DNA isolation kit.
  • Proteobacteria phylum was found to be the most dominant in biodegrading of hydrocarbons in oil-contaminated soil with nitrogen and phosphorus solution treatment.
  • A correlation between the treatment/conditions with the efficacy of bioremediation was established.

Abstract

Many different methods are currently being used for oil degradation in various oil spilled sites such as containment booms, combustion, offshore filtration and collection etc. Bioremediation is found to be of efficacy in treating sites with potentially toxic by-products. Since there is no single microorganism that can degrade all crude oil components – a microbial consortium is used for most effective treatment. Therefore, to be able to quantify the effects of bioremediation on different sites and to determine the optimal conditions for the microbial communities, experiments were performed on microcosms of compost soil and soil from a petrol station under renovation. The data achieved from the experiments were then analysed through cell enumeration, gas chromatography-mass spectrometry and bioinformatics. The results revealed that microbial communities from the Proteobacteria phylum present in compost soil and oil contaminated soil were the most effective and efficient in the degradation of oil. Nutrient-enhanced microbes were found to be the most effective compared to the microbes that were deprived of the nutrients. Amendments of the levels of nutrients used and the microbial species used in future experiments could give a better insight into optimal bioremediation methods for the site which can be successfully treated.

Keywords: Soil; Petroleum hydrocarbons (PHCs); Bioremediation; Oil spillage; Decontamination

1. Introduction

1.1 Oil spillage and environment

Oil spills are a form of pollution where the petroleum released to the environment as a result of an oil spill can be very harmful to the ecosystem for both marine and terrestrial beings (Teal and Howarth, 1984). These oil spills are known to be unpredictable and therefore the resources required for minimizing its environmental impacts are needed now more than ever, especially since the Deepwater Horizon oil spill incident that took place in 2010. The magnitude of that type of petroleum release could harm any country’s natural resources and therefore needs to be minimized as effectively and efficiently as possible (Bishop et al., 2017).

1.2 Strategies used for oil degradation

Petroleum contains a diverse quantity of various hydrocarbons, which upon being released to the environment could degrade the quality of soil as well as the microbial populations residing in the soil. In order to tackle this issue, many different strategies are used to diminish the impacts, these strategies include clean-up through containment booms, combustion, collection, offshore filtration, dispersants which used microbes for the digestion (Wilkinson et al., 2017).

1.3 Bioremediation and its effectiveness

Bioremediation is one of the many strategies being utilized to treat the oil spill sites and ultimately limiting any toxic by-products. Bioremediation is essentially the use of microorganisms or plants to remove or degrade most toxic environmental pollutants (Hlihor et al., 2017). This method is a very cost-effective, efficient and environmentally friendly way to remove or transform these contaminants. Although an issue faced when using bioremediation is that the hydrocarbons from the petroleum tend to bind to various soil components making them harder to degrade. To tackle this issue, it has been found that biosurfactants such as rhamnolipids that are produced by different Pseudomonas spp. can help with emulsification of these hydrocarbons (Pacwa-Płociniczak et al., 2011).

1.4 Role of nitrogen and phosphorus in bioremediation

Studies show that nitrogen and phosphorus are vital as a nutrient source for plant and microbial growth. Nitrogen itself is a crucial element for the optimal growth of microbes and it plays a fundamental role in the production of biosurfactants that as a result help emulsify hydrocarbons (Sterner et al., 1995). Due to these essential roles of nitrogen and phosphorus in microbial growth, as a biostimulation, this experiment explores the true effects of these nutrients on the efficiency of bioremediation by using it as one of the treatments for the soil used.

1.5 Aims and Hypotheses

The aim of this experiment is to find the optimal treatment for the microbial consortia found in different soils to be able to biodegrade the hydrocarbons found in petroleum. The purpose of this study is to also find the optimal soil conditions for the microbes to be able to thrive and provide the most effective bioremediation. It is hypothesised that the oil-contaminated soil will have the microbes that are the most effective in biodegradation of hydrocarbons and the nutrient-enhanced microbes (i.e., soil with nitrogen and phosphorus solution) would be the most efficient of them all.

2. Materials and Methods

2.1 Preparation for different substrates

The soil samples taken from two different sites, i.e., compost soil and soil from a petrol station that is being renovated, which was then subjected to different treatments. There were 4 different flasks for each treatment and all the flasks were subjected to an addition of mineral media (200 mL). Flask 1 contained the control i.e., only the normal soil sample, flask 2 contained the abiotic control i.e., the autoclaved soil sample with petrol, flask 3 contained normal soil with petrol and finally flask 4 contained normal soil sample with petrol, and nitrogen and phosphorus solution (NP solution).

2.2 Cell enumeration

On day 0 for the experiment, serial dilutions were performed for the two soil samples, compost soil sample and soil from a petrol station being renovated. Spread plates and drop plates were used to find out the initial cell count in colony forming unit per gram (CFU/g) and then ultimately converted and recorded in CFU/mL for the different soil types. The same procedure was followed on day 28 to observe and record the final cell count to compare the cell colonies grown over time in the two soil samples.

2.3 Oil Extraction and analysis using GC-MS

Liquid-Liquid extraction was then used to extract any oil present from the aqueous samples for the analysis using Gas chromatography-mass spectrometry (GC-MS) (Page 30). Squalane was added to the extracted oil as an internal standard which later helped normalize the data obtained from GC-MS. The raw data achieved was analysed using Agilent 6890-5973 GC-MS. From there, the data attained was then normalized with the help of the internal standard, Squalane, which was added at a known amount and concentration during the liquid-liquid extraction. Using GC-MS helped analyse trace level and unknown compounds present in the samples with the assistance of data analysis through graphing all the outcomes found.

2.4 DNA Isolation and Analysis

PowerSoil® DNA Isolation Kit was used and the instructions provided by MO BIO in the protocol along with the kit were followed precisely. This helped identify the microbial consortia present in each soil sample more accurately which could then be classified using bioinformatics. Bioinformatics was used to collate and analyse all the large data sets using computers and software tools, which would not be otherwise possible, and to also classify the microbial consortia found in the soil samples according to their taxonomic features.

2.5 Fragment sequencing and classification

The DNA extracted by the MO BIO extraction kit were amplified using primers 16S:27F and 519R while paired-end libraries were later sequenced using Illumina’s MiSeq sequencer. The dataset of the sequences compiled was demultiplexed, concatenated, joined, filtered, dereplicated and finally clustered into Operational taxonomic units (OTUs) based on a given sequence identity threshold.

Following the clustering of the sequences, the chimeric sequences (i.e., artificial sequences that were originated from two different genes) were removed to filter all the fragments accordingly. The filtered fragments were then compared against the OTUs using the analysis framework for amplicons known as Qiime. Using Qimme’s “summarize_taxa_through_plots.py” function, bar and area charts were created for a better representation of the data accumulated for the taxonomic classification.Pages 16-54 and 74 of 91818 Environmental Biotechnology Subject Manual outline the steps in extensive details for the whole procedure and can be referred to for a more detailed methodology (Labeeuw, 2018).

3. Results and Discussion

3.1 Identification of effective microbial population

For the purposes of efficient bioremediation, identifying the most effective microbial consortia is of great importance in that it will help conduct further studies in bioremediation. This identification of microbial population will be very beneficial for researchers as they will be able to select and isolate these microorganisms for the purposes of enhancing their performance using biostimulation and bioaugmentation to achieve the best results possible for oil degradation in soil (Malina and Zawierucha, 2007).

3.2 Evidence of bioremediation’s effectiveness

In this experiment, the phylum with the most growth was identified to be Proteobacteria as can be seen in Figure 4 making up to 61.9% of microbial consortia.Whereas the classes Alphaproteobacteria and Gammaproteobacteria were found to have the most growth within this phylum as observed in Figure 5 making31.5% and 20% of microbial consortia respectively. The effectiveness of bioremediation using these microbes is slightly hinted in Figure 1, but it cannot be stated as a fact due to a very high standard error across all cell enumeration. Although it can be seen in Figures 2 and 3, which evidently shows the degradation of oil through GC-MS analysis. In both, compost soil and oil contaminated soil it is dominantly bioremediated by these microbes that are nutrient enhanced due to biostimulation and bioaugmentation. Hence, hydrocarbon degradation using bioremediation is deemed effective and reliable in that it also helps reduce environmental pollution which is the eco-friendliest way to remediate the environment (Azubuike et al., 2016).

3.3 Comparison of different environments and their effects on bioremediation

The optimal soil treatment across both soil types was found to be the one treated with nitrogen and phosphorus solution. Although, oil contaminated soil on its own was found to be just as effective while compost soil was not able to remove as many hydrocarbons as the oil-contaminated soil which can be seen when comparing Figure 2 and 3. These results can be attributed to the difference in types of microbial consortia, although, the phylum Proteobacteria is found to be effective in both soil types, it is more effective in the oil contaminated soil as these microbes have adapted to the conditions and are much robust than that of compost soil. Therefore, the microbes found in petroleum soil are able to thrive and degrade hydrocarbons much more effectively than that of compost soil (Agamuthu et al., 2013).

3.4 Impact of nutrients on bioremediation

Observing Figures 2 and 3, it is evident that treating the soil samples with nitrogen and phosphorus solution enhances the ability of microbes to degrade most hydrocarbons regardless of the soil type. Although, in specific, the bioremediation of oil was enhanced the most in the oil-contaminated soil as can be seen in Figures 2 and 3 with the highest population of Proteobacteria classes thriving due to the presence of nitrogen and phosphorus solution. However, studies show that some microbes are able to adapt to their respective environment and therefore it is observed in the results that the untreated soil sample was still able to remove just as many hydrocarbons as the one treated with nitrogen and phosphorus solution (Carrero-Colon et al., 2006). Therefore, using the specific type of microbes for bioremediation of a certain soil type with optimal nutrients would accomplish the best results possible. Figures 4 and 5 also show the highest growth for Proteobacteria and its classes thriving the most in oil-contaminated soil with nitrogen and phosphorus solution.

3.5 Interaction of microbial communities with petroleum hydrocarbons

Studies show that different groups of the phylum Proteobacteria interact differently with petroleum hydrocarbons and are dependent on whether there is an addition of nutrients or not. Alphaproteobacteria group was dominant in biodegradation in both nutrient and non-nutrient treatments. In specific, the genera Azospirillum and Sphingomonas were found to be the most effective. Bacteroidetes, on the other hand, was found to be the best group in the treatment without nutrients as can be witnessed in Figure 4. Whereas, genus Xanthomonas of Gammaproteobacteria and genus Sphingomonas of Alphaproteobacteria were observed to be the most dominant in the treatments with the addition of nutrients (Vinas et al., 2005). Considering these characteristics, it is possible to isolate the specific microbial communities based on their interactions with the treatments and conditions available. If these groups were isolated and were used to form a microbial consortium from these specific groups, the bioremediation process would undoubtedly become more effective and efficient, although more research in various other environment is necessary including high or low temperatures and more importantly the types of petroleum hydrocarbons being biodegraded.

3.6 Limitation and errors

Although the experiment was able to achieve distinctive data with concrete results, it was not without its limitations and errors. Time was undoubtedly a limiting factor as more research and experimentation would have been better for reliability if the experiments were repeated a couple of times. The results were very skewed which could be attributed to systematic errors and also contamination playing a vital role in very high variance in the results observed in Figure 1 showing low accuracy which could be mitigated with the use of better equipment. However, the results seemed to be valid as most of the data achieved are similar to that of most literature values out there.

3.7 Future suggestions and experiments

A better step in future studies would be to explore Proteobacteria, its classes and subclasses all the way down to species to form the most effective microbial consortium that contains only the highest bioremediating microbes. Ensuring the optimal treatments (i.e., nutrients) and conditions (i.e., soil type and environment etc.) for these microbes would also help in achieving the most effective and efficient way of oil biodegradation. Genetically engineering these microbes could also open up more possibilities of enhancing the ability of the microbes to biodegrade petroleum compounds more proficiently (Urgun-Demirtas et al., 2006).

4. Conclusion

In conclusion,the phylum Proteobacteria was found to be the best contender for being a part of a microbial consortium which could be a part of the most effective microbial population to bioremediate hydrocarbons. The best results were achieved from the oil-contaminated soil and therefore the conclusion is limited to that type of soil and would require further experimentation for an optimal environment and conditions. Addition of nitrogen and phosphorus solution as a soil treatment was found to be even more effective and can be further explored to achieve the best results possible in the future.

References

  1. Agamuthu, P., Tan, Y. and Fauziah, S. (2013). Bioremediation of Hydrocarbon Contaminated Soil Using Selected Organic Wastes. Procedia Environmental Sciences, 18, pp.694-702.
  2. Azubuike, C., Chikere, C. and Okpokwasili, G. (2016). Bioremediation techniques–classification based on site of application: principles, advantages, limitations and prospects. World Journal of Microbiology and Biotechnology, 32(11).
  3. Bishop, R., Boyle, K., Carson, R., Chapman, D., Hanemann, W., Kanninen, B., Kopp, R., Krosnick, J., List, J., Meade, N., Paterson, R., Presser, S., Smith, V., Tourangeau, R., Welsh, M., Wooldridge, J., DeBell, M., Donovan, C., Konopka, M. and Scherer, N. (2017). Putting a value on injuries to natural assets: The BP oil spill. Science, 356(6335), pp.253-254.
  4. Carrero-Colon, M., Nakatsu, C. and Konopka, A. (2006). Effect of Nutrient Periodicity on Microbial Community Dynamics. Applied and Environmental Microbiology, 72(5), pp.3175-3183.
  5. Hlihor, R., Gavrilescu, M., Tavares, T., Favier, L. and Olivieri, G. (2017). Bioremediation: An Overview on Current Practices, Advances, and New Perspectives in Environmental Pollution Treatment. BioMed Research International, 2017, pp.1-2.
  6. Labeeuw, L. (2018). 91818 Environmental Biotechnology Subject Manual. School of Life Sciences, Faculty of Science, pp.16-54, 74.
  7. Malina, G. and Zawierucha, I. (2007). Potential of Bioaugmentation and Biostimulation for Enhancing Intrinsic Biodegradation in Oil Hydrocarbon-Contaminated Soil. Bioremediation Journal, 11(3), pp.141-147.
  8. Pacwa-Płociniczak, M., Płaza, G., Piotrowska-Seget, Z. and Cameotra, S. (2011). Environmental Applications of Biosurfactants: Recent Advances. International Journal of Molecular Sciences, 12(1), pp.633-654.
  9. Sterner, R., Chrzanowski, T., Elser, J. and George, N. (1995). Sources of nitrogen and phosphorus supporting the growth of bacteria and phytoplankton in an oligotrophic Canadian shield lake. Limnology and Oceanography, 40(2), pp.242-249.
  10. Teal, J. and Howarth, R. (1984). Oil spill studies: A review of ecological effects. Environmental Management, 8(1), pp.27-43.
  11. Vinas, M., Sabate, J., Espuny, M. and Solanas, A. (2005). Bacterial Community Dynamics and Polycyclic Aromatic Hydrocarbon Degradation during Bioremediation of Heavily Creosote-Contaminated Soil. Applied and Environmental Microbiology, 71(11), pp.7008-7018.
  12. Urgun-Demirtas, M., Stark, B. and Pagilla, K. (2006). Use of Genetically Engineered Microorganisms (GEMs) for the Bioremediation of Contaminants. Critical Reviews in Biotechnology, 26(3), pp.145-164.
  13. Wilkinson, J., Beegle-Krause, C., Evers, K., Hughes, N., Lewis, A., Reed, M. and Wadhams, P. (2017). Oil spill response capabilities and technologies for ice-covered Arctic marine waters: A review of recent developments and established practices. Ambio, 46(S3), pp.423-441.

Figure Captions

Figure 1: Cell enumeration of initial vs final soil samples with different treatment comparing Class 1 results.

Figure 2: Total petroleum hydrocarbons present in each treatment for compost oil observed using GC-MS.

Figure 3: Total petroleum hydrocarbons present in each treatment for oil contaminated soil observed using GC-MS.

Figure 4: The proportion of microbial consortia present in different soil samples with different soil treatment.

Figure 5: The proportion of microbial consortia present in different soil samples with different soil treatment.

Tables and Figures

Figure 1: Cell enumeration of initial vs final soil samples with different treatment comparing Class 1 results. This figure shows the cell enumeration for the microbial population of different soil treatment for compost soil and oil contaminated soil. The graph compares the count for day 0 (initial) and the count for day 28 (final). Although there is a change in the final count vs initial count for compost soil across all treatments, no viable conclusion can be drawn from this trend since there is a very high standard error across all the initial counts for compost soil. Whereas, the final counts for oil contaminated soil have remained unchanged.


Figure 2: Total petroleum hydrocarbons present in each treatment for compost oil observed using GC-MS. This figure aims to compare different hydrocarbon levels across the different treatments using Squalane as an internal standard to normalize the data. As expected, the abiotic control contains all the hydrocarbons while the most degradation of petroleum occurring in the treatment with the soil containing nitrogen and phosphorus solution. The treatment with just the soil and oil was also relatively effective in the degradation of oil. The control shows slight traces of petroleum which should not be the case and can be attributed to cross-contamination of different samples.

Figure 3: Total petroleum hydrocarbons present in each treatment for oil contaminated soil observed using GC-MS. This figure aims to compare different hydrocarbon levels across the different treatments using Squalane as an internal standard to normalize the data. As expected, the abiotic control contains all the hydrocarbons while the rest of the treatments contain the hydrocarbon “docosane”. Treatment with the soil containing nitrogen and phosphorus solution had the most degradation of petroleum with similar results for the control. Although, control should have no traces of petroleum and can thus be concluded that the sample was indeed contaminated. The treatment with just the soil containing oil also contains traces of docosane very slightly higher than that of soil with NP solution hence proving to be relatively effective in degradation of petroleum as well.

Figure 4: The proportion of microbial consortia present in different soil samples with different soil treatment. This figure shows the variety of microbial population present in different treatments across the two soil samples i.e., compost soil and oil contaminated soil. Each treatment has been performed in duplicates for reliable results. The phylum Proteobacteria is the most dominant of all the other phyla present with 61.9% of the total population across all treatments. Other phyla present can be seen in the legend which includes some of the other high microbial populations after Proteobacteria.

Figure 5: The proportion of microbial consortia present in different soil samples with different soil treatment. This figure also shows the variety of microbial population present in different treatments across the two soil samples i.e., compost soil and oil contaminated soil. Each treatment has been performed in duplicates for reliable results. This figure has the same conformation of results as Figure 4 except it explores the different classes of the highest phylum (Proteobacteria) found across all soil treatments. Alphaproteobacteria and Gammaproteobacteriaare observed to be the most prevalent of all the other classes. These highly prevalent microbial populations are extensively assessed in the discussion.

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