Next Article in Journal
Comparisons of Zooplankton Community Structure between with- and without- Pesticide Applications on Rice Fields
Previous Article in Journal
Seasonal Changes in the Photosynthetic Activity of Terrestrial Lichens and Mosses in the Lichen Scots Pine Forest Habitat
Previous Article in Special Issue
Collection of VKM Paleofungi
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 13
Issue 12
10.3390/d13120643
diversity-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Statistical Assessment of Phenol Biodegradation by a Metal-Tolerant Binary Consortium of Indigenous Antarctic Bacteria

by
Kavilasni Subramaniam
1,
Siti Aqlima Ahmad
1,2,
Peter Convey
3,4,
Noor Azmi Shaharuddin
1,
Khalilah Abdul Khalil
5,
Tengku Athirrah Tengku-Mazuki
1,
Claudio Gomez-Fuentes
2,6 and
Azham Zulkharnain
7,*
1
Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Center for Research and Antarctic Environmental Monitoring (CIMAA), Universidad de Magallanes, Avda. Bulnes, Punta Arenas 01855, Región de Magallanes y Antártica Chilena, Chile
3
British Antarctic Survey, NERC, High Cross, Madingley Road, Cambridge CB3 0ET, UK
4
Department of Zoology, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa
5
School of Biology, Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
6
Department of Chemical Engineering, Universidad de Magallanes, Avda. Bulnes, Punta Arenas 01855, Región de Magallanes y Antártica Chilena, Chile
7
Department of Bioscience and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, 307 Fukasaku, Minuma-ku, Saitama 337-8570, Japan
*
Author to whom correspondence should be addressed.
Diversity 2021, 13(12), 643; https://doi.org/10.3390/d13120643
Submission received: 4 October 2021 / Revised: 19 November 2021 / Accepted: 24 November 2021 / Published: 4 December 2021
(This article belongs to the Special Issue Culture Collections as Hidden Sources of Microbial Biomolecules and Biodiversity)
Browse Figures
Versions Notes

Abstract

:
Since the heroic age of Antarctic exploration, the continent has been pressurized by multiple anthropogenic activities, today including research and tourism, which have led to the emergence of phenol pollution. Natural attenuation rates are very slow in this region due to the harsh environmental conditions; hence, biodegradation of phenol using native bacterial strains is recognized as a sustainable remediation approach. The aim of this study was to analyze the effectiveness of phenol degradation by a binary consortium of Antarctic soil bacteria, Arthrobacter sp. strain AQ5-06, and Arthrobacter sp. strain AQ5-15. Phenol degradation by this co-culture was statistically optimized using response surface methodology (RSM) and tolerance of exposure to different heavy metals was investigated under optimized conditions. Analysis of variance of central composite design (CCD) identified temperature as the most significant factor that affects phenol degradation by this consortium, with the optimum temperature ranging from 12.50 to 13.75 °C. This co-culture was able to degrade up to 1.7 g/L of phenol within seven days and tolerated phenol concentration as high as 1.9 g/L. Investigation of heavy metal tolerance revealed phenol biodegradation by this co-culture was completed in the presence of arsenic (As), aluminum (Al), copper (Cu), zinc (Zn), lead (Pb), cobalt (Co), chromium (Cr), and nickel (Ni) at concentrations of 1.0 ppm, but was inhibited by cadmium (Cd), silver (Ag), and mercury (Hg).

1. Introduction

Antarctica and its surrounding Southern Ocean are widely considered to be among the Earth’s last pristine wildernesses, compared with the rest of the world, and has only recently been discovered and subsequently impacted by humans. The negotiation of the Antarctic Treaty in 1959, which came into force in 1961, declared that this ‘white canvas’ was to be a land of science, research, and peaceful cooperation between nations. Since then, the continent has seen inexorable growth in scientific exploration, numbers, and extent of research stations, research activities, marine transportation and, more recently, tourism [1,2]. Human presence and activity present a distinct set of threats to the Antarctic environment, one of which is pollution [3,4]. Despite the strict guidelines provided in the Protocol on Environmental Protection to the Antarctic Treaty to protect the Antarctic environment, there remains potential for significant marine and terrestrial impacts at various scales due to accidents such as shipwrecks and pollution associated with research activities and research stations [2]. Historical and, in some cases, ongoing application of inappropriate waste management practices have led to the release of contaminants both to the surrounding terrestrial and near-shore aquatic environments [5,6,7].
The primary routes by which phenol has entered the Antarctic environment are through oil spills and inappropriate waste management practices. The most recent major oil spill in the marine environment took place in November 2007 when the cruise vessel, MS Explorer, carrying 1200 L of petrol, 24,000 L of lubricant oil, and 190,000 L of marine gas oil was holed by ice and sank soon after in the Bransfield Strait between the Antarctic Peninsula and South Shetland Islands [8]. Further diesel spills have occurred at research stations, such as at the Argentinian Carlini Station, King George Island, in 2009 [9]. Following such spills, POPs, including phenol, can be transported more widely through physical and chemical processes, resulting from the addition of dispersants, subsequent shoreline erosion, water washing, or volatilization, while retaining their toxicity [10]. In addition to catastrophic marine accidents, refueling operations for stations on land, pipeline faults within the stations’ transport, and smaller-scale operations such as refueling of vehicles are the sources of the majority of terrestrial fuel spills [11]. Phenol is one of the major components used in the production of diesel fuel [12] and Singh et al. [13] reported that petroleum wastewater containing 140–480 mg/L of oil included phenol at concentrations ranging from 1.2 to 3.71 mg/L. Moreover, discharging lightly treated grey water into the sea from coastal research stations and, historically, also onto ice and other parts of the local environment from inland stations, can lead to phenol pollution [14], as phenol is one of the main components in many household and pharmaceutical products [15,16]. Phenol and phenol-derived pollutants such as vanillic acid, homovanilic acid, syringic acid, p-coumaric acid, and polychlorinated biphenyls are persistent in the Antarctic environment due to the continent’s frigid and often dry climate, limiting the rates of both biological processes and abiotic degradation [17,18,19].
The hazardous effects of phenol, even at low concentrations in the environment, are more than sufficient to justify mitigation. In recent decades, research into the treatment of phenol pollution has gained momentum [20,21]. Remediation in polar regions has traditionally relied upon excavation and removal of polluted soil, which may still be appropriate in instances of severe contamination. However, this type of remediation has a considerable direct impact on the Antarctic environment and can result in soil shrinkage, land slumping, permafrost melt, or alteration of groundwater flow. Such approaches are also expensive and logistically challenging [22]. Therefore, bioremediation using native microorganisms has attractive potential as a sustainable approach for restoring contaminated sites. Polluted wastewater is often compounded by the presence of various heavy metals as co-contaminants, which can have further adverse effects on the function and availability of microorganisms [23,24,25,26]. Concordantly, traces of heavy metals were detected in the wastewater discharged from various Antarctic research stations [27,28]. Thus, heavy metal toxicity may compromise the biodegradation of phenol. Studies of the relationship between bacterial tolerance to heavy metals and degradation of phenolic compounds are, therefore, an important element of the development of bioremediation applications.
In recent years, studies using mixed microbial cultures have attracted increasing research attention, especially recognizing the potential role of cooperative or synergistic effects result from the possession of different but complementary biochemical degradation pathways in the different strains present [29]. A range of studies are now available using mixed culture methods, encouraging investigation of the application of this approach in Antarctica using native microorganisms. Nevertheless, several limitations exist, such as the limited capability of microbial species in the field and the absence of benchmark values against which to test the technique’s effectiveness for extensive field application [30]. In addition, the effectiveness of biodegradation depends on several external factors such as temperature, pH, nutrients availability, salinity, and site characteristics [21,31]; hence, investigation of effect of these factors is crucial to indicate the optimal conditions that play a role in bioremediation. A statistical approach using design of experiment (DoE) tools is efficient and versatile for the optimization of processes comprising multiple variables as it reduces the number of experimental trials and identifies the influence of individual factors, in addition to the interaction effects on the desired response [32,33]. Central composite design (CCD) and response surface methodology (RSM) comprising factorial designs and regression analyses are some of the DoE tools used to design such experiments, evaluate the significant factors, and build models to reveal optimum conditions for the biodegradation process [34].
The present study focuses on the statistical evaluation of phenol degradation by a binary consortium of the Antarctic soil bacteria, Arthrobacter sp. strain AQ5-06, and Arthrobacter sp. strain AQ5-15 in combination with investigation of the consortium’s tolerance of exposure to different heavy metals.

2. Materials and Methods

2.1. Bacterial Culture, Maintenance and Media Preparation

Arthrobacter sp. strains AQ5-06 (KX946127) and AQ5-15 (MK744046) from King George Island, which were previously isolated and identified as diesel and phenol-degrading bacteria [35,36,37], and maintained in glycerol stock under −80 °C at the Eco-Remediation Technology Laboratory, Universiti Putra Malaysia, were used in this study. These two strains were inoculated individually in nutrient broth (NB) and incubated at 10 °C at 150 rpm for 96 h. Then 1 mL culture broth was transferred to degradation culture medium containing 0.5 g/L phenol and incubated for 96 h. Repeated transfers were performed several times under the same conditions as earlier to acclimatize the cultures. Minimal salt medium (MSM) (pH 7.5) was prepared by adding (g/L) K2HPO4 (0.4), KH2PO4 (0.2), NaCl (0.1), Mg2SO4, (0.1), (NH4)2SO4 (0.4), MnSO4·H2O (0.01), Fe2(SO4)·H2O (0.01), and NaMoO4·H2O (0.01). Phenol was added after autoclaving the medium at 121 °C for 20 min. The working quantity of phenol medium (PM) was 50 mL for all experiments conducted in 250 mL Erlenmeyer flasks.

2.2. Phenol Biodegradation

A binary consortium of strains AQ5-06 AQ5-15 with initial inoculum size of OD600 nm ≈ 1.00 ± 0.10 was prepared by blending them to a final concentration of 10.0% (v/v) and inoculating them into the PM prior to incubation. Phenol medium without bacterial inoculation served as a control sample. Phenol degradation was assessed using the 4-aminoantipyrine assay following the American Public Health Association method and analyzed by UV-vis spectrophotometry at 510 nm [38]. Optical density at 600 nm was used as a proxy measure of bacterial growth [39]. In brief, 1 mL of the culture medium was centrifuged at 12,000× g for 15 min. The supernatant was separated from the pellet and further used in the 4-AAP assay, while the pellet was diluted with 1 mL sterile distilled water and used to measure bacterial growth. All experiments were carried out in triplicate in a batch shake flask. The percentage of phenol degradation was calculated using Equation (1):
Phenol   degradation   ( % ) = x 2 − x 1 x 2 × 100 %
where x 2 is the initial phenol concentration and x 1 is the residual phenol concentration.

2.3. Design of Experiment (DoE) by Response Surface Methodology (RSM)

The central composite design (CCD)-based RSM is a statistical tool widely used for investigational design, modeling, and optimization. In this study, the experimental design and statistical analyses were generated using Design-Expert® version 6.0.8 (Stat-Ease Inc. Minneapolis, MN, USA) to optimize the culture conditions. In total, 30 runs with four significant variables were considered at five different levels (−2, −1, 0, +1, +2) containing 16 factorial points, eight axial points, and six replications at the center point (Table 1). The experimental values’ range of each factor was selected based on the optimum range of pure culture. Phenol degradation was used as the response and the significance of the model and each coefficient in the equation were examined using Fischer’s F-test and ANOVA. Upon completion of DoE, biodegradation of phenol was carried out in the batch culture to obtain an appropriate model. The model was validated by comparing the predicted response with experimental data.

2.4. Heavy Metal Tolerance

Tolerance of exposure to the heavy metals arsenic (As), aluminum (Al), silver (Ag), copper (Cu), cobalt (Co), chromium (Cr), cadmium (Cd), lead (Pb), nickel (Ni), zinc (Zn), and mercury (Hg) was evaluated separately at a concentration of 1.0 ppm in MSM containing 0.5 g/L phenol. Pure cultures of strain AQ5-06, strain AQ5-15, and the binary consortium were inoculated individually and incubated under optimized conditions at 150 rpm. A quantity of 10.0% (v/v) of bacterial strain was inoculated into MSM in the absence of heavy metals to act as control [40]. Data analysis for dose-response inhibition was performed using GraphPad Prism version 9.0.2 and relative inhibition was plotted vs. the heavy metal concentration and fitted to a standard inhibition dose-response curve to generate a half maximal inhibitory concentration (IC50) value [41].

3. Results and Discussion

3.1. Statistical Optimization of Phenol Degradation Using RSM

Prior to statistical analysis, the effect of inoculum ratio of the two strains was determined by adding, to 50 mL MSM containing 0.5 g/L of phenol, the acclimatized cell cultures at varying amounts in such a way as to obtain AQ5-06:AQ5-15 ratios of 1:1, 1:3, and 3:1. In each of the three cases, there was no apparent lag, but a better growth was observed when the inoculum ratio was 1:1 (Table 2). Consortia with inoculum ratios of 1:3 and 3:1 have both slower growth and degradation rates. In agreement, Tecon and Or [42] also reported that strains mixed in equal fractions are expected to have optimal mutualistic interaction. The active performance of the selected binary consortium was confirmed by comparing the outcome with the control sample. Pure culture of strain AQ5-06 was reported to completely degrade 0.5 g/L of phenol within 120 h [35], and strain AQ5-15 to completely degrade 0.5 g/L of phenol within 108 h [36]. As shown in Table 2, these two strains performed better as the co-culture favored convergence of 1:1 partner ratio.
The investigational results of phenol biodegradation by the binary consortium were examined through RSM to generate an empirical model. Based on these outcomes, the relationship between the response and independent variables was identified and is described by the second-order polynomial equation (Equation (2)):
Y = +94.78 − 0.022x1 − 1.47x2 + 1.26x3 + 8.12x4 − 4.45x12 − 4.67x22 − 4.59x32 − 13.12x42
where Y is the phenol degradation percentage, x1, x2, x3, and x4 are the corresponding independent variables (NH4)2SO4 concentration (g/L), NaCl concentration (g/L), pH, and temperature (°C), respectively. These four factors were employed based on the RSM analysis undertaken in previous studies on pure cultures of strains AQ5-06 and AQ5-15 [43,44]. Clarke [45] noted that a well-balanced medium comprising carbon and nitrogen is essential for cell growth and maintenance, and Varjani and Upasani [46] found that nutrient availability, especially nitrogen demand, is crucial in hydrocarbons’ biodegradation. A tolerable level of NaCl has a positive effect on the biological performance of microorganisms and exceeding that level can negatively affect the cell numbers and distribution, thus reducing the rate of microbial metabolism [47]. Most microbes have lower tolerance to high or low pH due to the significant effect on the biochemical reactions required for biodegradation [48]. Finally, temperature has a direct influence on the microbial growth rates and metabolic processes, and can affect the physical state of pollutants [46].
Table 3 shows the ANOVA results of CCD on phenol degradation by the binary consortium. The overall model was well supported and there is significant correlation between the factors and response. One linear term D, and all four quadratic terms A2, B2, C2, and D2, were significant for phenol degradation. This indicated that the temperature has the most significant effect on phenol degradation by this binary consortium. Although the linear terms A, B, and C were not significant, significant second orders of these factors indicated that they affect the response. The insignificant “lack of fit” value and high coefficient of determination (R2) and adjusted R2 values confirm the model provides an appropriate fit to the experimental data.
Figure 1a indicates the close similarity between the predicted and experimentally achieved value for phenol degradation by the binary consortium, and Figure 1b reveals no indication of non-normality, confirming the quadratic model is satisfactory for the purpose of this analysis.
The highly significant influence of the temperature on phenol degradation is shown in Figure 2. This indicated that even a small deviation in temperature can have a disproportionate effect on phenol degradation achieved by the binary consortium. Temperature is a crucial variable, especially in the polar regions. The optimum temperature for phenol degradation by this consortium was 12.50–13.75 °C (Figure 2), supporting the potential use of this consortium in phenol degradation in Antarctica during summer periods where the soil surface temperature may reach up to 19.8 °C [49]. Although not crucial, the presence of (NH4)2SO4 and NaCl, and near-neutral pH had little impact on phenol degradation. None of the interaction effects between variables were significant. CCD analysis on pure culture of strain AQ5-06 revealed that the optimum conditions for phenol degradation by this strain were between 10.0 and 12.5 °C with NaCl concentration range of 0.10–0.13 g/L and pH 6.5–7.0, with these three, and not (NH4)2SO4 concentration, being significant factors [43]. Optimum conditions for strain AQ5-15 are at a pH range of 7.13–7.50, and 15.0–17.5 °C with interaction between these two factors being significant [44]. In a previous study, ANOVA of RSM data for phenol degradation by a microbial consortium revealed that pH and temperature were significant factors but that the interaction between them was not significant [31].
The RSM model was validated using the values predicted for each parameter. This identified no significant difference (t-test, p = 0.0671) between the percentage of phenol degradation predicted by the model (91.14%) and measured experimentally approaches (93.84%), supporting the validity of the model.

3.2. Effect of Phenol Initial Concentration on Consortium Degradation Activity

Figure 3 shows the bacterial growth and degradation of different initial concentrations of phenol by the binary consortium under the optimized conditions obtained from CCD. The strains, when in consortium, had the ability to degrade phenol up to an initial concentration of 1.7 g/L within 168 h. When the initial concentration was increased to 1.9 g/L, the strains started to lose their degradation ability despite growth being visible. High concentration of phenol hinders the microbial growth and its metabolic capacity. The bactericidal property of phenol allows it to partition into the cellular membrane, thereby disrupting membrane function and resulting in cell mortality [50,51]. Strain AQ5-06 in monoculture did not exhibit significant growth at a phenol concentration of 1.25 g/L [43], whereas strain AQ5-15 was inhibited at a phenol concentration of 1.7 g/L [44]. Similar to these pure cultures, Wen et al. [52] also reported that strain Rhodococcus sp. SKC showed no significant degradation or growth over 15 days at a phenol concentration of 1.5 g/L due to the inhibitory nature of the target pollutant. The ability of the binary mixture of the strains studied here to tolerate up to 1.9 g/L initial phenol concentration and to metabolize phenol at a concentration as high as 1.7 g/L supports the advantage gained from a defined mixed culture in phenol biodegradation. Similarly, Senthilvelan et al. [53] reported that a mixed culture of Pseudomonas putida strain Tan-1 and Staphylococcus aureus strain Tan-2 can degrade a high concentration of phenol in a short period of time compared to both strains individually. It is possible that the metabolic capacity of monocultures may be limited to certain concentration ranges of substrates (in this case, phenol), but that the use of consortia broadens their enzymatic capabilities and provides greater capacity to degrade higher concentrations of phenol [54,55].

3.3. Impact of Heavy Metals on Phenol Biodegradation

Metal ions have proven to have significant effects on phenol biodegradation as they inhibit the activity of catechol dioxygenase which serves as a key enzyme in phenol metabolism [56,57]. Studies on baseline values for heavy metals in many Antarctic regions have reported significant levels. A study on King George Island showed the presence of Hg, Cd, Pb, Zn, Ni, Cu, As, and Cr in benthic samples [58]. Similarly, assessment of soil from Fildes Peninsula and Ardley Island revealed the presence of significant concentrations of Pb, Cu, Zn, Hg, and Cd [59]. Lischka et al. [60] reported the presence of Ag, Cd, Hg, Cr, Pb, Ni, Co, As, Zn, and Cu in different species of Antarctic octopod sampled near Elephant Island, South Shetland Islands. For the purposes of the current study, Cu, Cr, Zn, Pb, As, Ni, Al, Co, Ag, Hg, and Cd were chosen as heavy metals of interest. Our previous studies on Antarctic isolates revealed that these metals negatively affected the biodegradation processes [61,62,63,64].

3.3.1. Arthrobacter sp. Strain AQ5-06

The heavy metal tolerance of strain AQ5-06 was assessed by evaluating residual phenol concentration in culture media amended with 1.0 ppm of various heavy metal ions (Figure 4). ANOVA confirmed that significant differences in both bacterial growth [F11,12 = 200.7, p < 0.0001] and phenol degradation [F11,12 = 141158, p < 0.0001] for strain AQ5-06 were apparent with exposure to the different metals. Degradation by this strain was not inhibited by 1.0 ppm of Cu, Zn, Pb, As, Ni, Al, Co, or Cd, with 100% degradation of 0.5 g/L phenol achieved within 120 h. Exposure to Cr led to 83.52% (±0.0042) degradation and to Hg led to 11.82% (±0.0021) degradation within 120 h. Exposure to Ag completely inhibited phenol degradation. Strain AQ5-06 exhibited different growth patterns in response to exposure to the different heavy metals, with the highest growth observed in the presence of Cu. Tukey’s multiple comparison test confirmed that exposure Cu led to significantly greater growth (1.1890 ± 0.0050) than both the control conditions and all other metals tested. Growth was inhibited in the presence of Ag and was lowest in the presence of Hg (0.2555 ± 0.0149), and in both cases was significantly lower than in control conditions and relative to all other metals tested.
Following the initial screening, phenol degradation by strain AQ5-06 was tested in the presence of different concentrations of inhibiting metals (Figure 5). For Ag, ANOVA confirmed that both bacterial growth [F6,7 = 325.80, p < 0.0001] and phenol degradation [F6,7 = 501.10, p < 0.0001] were significantly influenced by the metal concentration applied. Inhibition of phenol degradation commenced at an Ag concentration of 0.7 ppm (38.30% inhibition), whereas at 0.8 ppm inhibition increased to 66.57%. A similar pattern was apparent in bacterial growth, with strong inhibition becoming apparent at 0.8 ppm. For Hg, ANOVA again confirmed that both bacterial growth [F5,6 = 767.80, p < 0.0001] and phenol degradation [F5,6 = 1518, p < 0.0001] were significantly influenced by the metal concentration applied. Phenol degradation started to show inhibition by Hg at 0.6 ppm (26.30% inhibition). At higher concentrations, degradation and growth were progressively reduced, reaching 88.18% inhibition at 1.0 ppm.
The IC50 value is the concentration of a substance that reduces an enzyme’s activity by 50%. The IC50 of these two heavy metals was determined using dose-response curves (Figure 6). Based on the modelled curve, both Ag and Hg exhibited similar levels of inhibition of phenol degradation with IC50 values of 0.75 and 0.74 ppm, respectively. Cu is known as an essential trace element, especially in aerobic organisms where it acts as a cofactor for enzymes that are responsible for diverse redox reactions [65]. Similarly, other heavy metals are essential for growth of microorganisms at trace concentrations [66]. Conversely, heavy metals such as Cd, Hg, and Pb have no known biological roles and their bioaccumulation in the cell over time can lead to negative impacts [66,67]. However, despite their toxicity, Pb and Cd did not inhibit phenol degradation by this strain, similar to the report of Yoo et al. [68]. The significant inhibitory effects on phenol degradation detected in this study are similar to those reported by Al-Defiery and Reddy [57] who confirmed that phenol degradation by Rhodococcus pyridinivorans strain GM3 was inhibited by Ag and Hg at low concentration. The inhibitory effects of Ag and Hg are caused by their ability to bind to sulfhydryl (-SH) groups of enzymes responsible for microbial metabolism, thereby impeding enzymatic actions [69]. These metals cause disturbance in cellular functions and trigger oxidative stress in microorganisms [70]. Ag and Hg are generally recognized as strong inhibitors of phenol degradation with bacteria unable to resist their impacts [57].

3.3.2. Arthrobacter sp. Strain AQ5-15

The heavy metal tolerance of strain AQ5-15 was assessed by evaluating residual phenol concentration in culture media amended with 1.0 ppm of various heavy metal ions (Figure 7). ANOVA showed that bacterial growth [F11.12 = 727.50, p < 0.0001] was significantly influenced by exposure to different metals. As phenol degradation was either 0% or 100% ANOVA could not be applied. Tukey’s multiple comparison test showed that growth under exposure to Cu was greater (1.2785 ± 0.0135) but not significantly different from the control, and otherwise significantly greater (all p < 0.05) in comparison with all other heavy metals tested. The growth of this strain was least in the presence of Ag (0.2675 ± 0.0025) and Hg (0.2065 ± 0.0045) and was moderate in the presence of Cd (0.503 ± 0.001), although still significantly lower than the control (p < 0.05).
Different concentrations of inhibiting metals were added to media with 0.5 g/L phenol to determine the minimum concentration required for inhibition within a 96 h incubation (Figure 8). For Ag, ANOVA identified a significant influence on both bacterial growth [F5,6 = 1504, p < 0.0001] and phenol degradation [F5,6 = 264.7, p < 0.0001]. Significant inhibition of phenol degradation started to become apparent at Ag concentration of 0.6 ppm (44.06% inhibition), increasing to 61.20% inhibition at 0.8 ppm. Bacterial growth similarly started to be inhibited at 0.6 ppm. For Hg, ANOVA confirmed a significant influence on both bacterial growth [F5,6 = 3807, p < 0.0001] and phenol degradation [F5,6 = 12230, p < 0.0001]. Significant inhibition of phenol-degradation became apparent at Hg concentration of 0.2 ppm (57.03% inhibition). At a concentration of 0.4 ppm both degradation and growth were completely inhibited. For Cd, ANOVA again confirmed a significant influence on both bacterial growth [F5,6 = 250.8, p < 0.0001] and phenol degradation [F5,6 = 1959000, p < 0.0001]. Phenol degradation was significantly inhibited at a Cd concentration of 0.2 ppm (79.88% inhibition). At a concentration of 0.4 ppm, degradation was completely inhibited although growth continued, decreasing with higher Cd concentration. This shows that this metal only hinders the metabolic process and does not kill the bacteria. The growth without the availability of phenol may indicate that this strain utilized nutrients recycled from dead cells [71].
IC50 of these heavy metals was determined using dose-response curves (Figure 9). Based on the modelled curve, the metal with the greatest inhibitory effect on phenol degradation by strain AQ5-15 was Cd which had the lowest IC50 value of 0.06 ppm, followed by Hg with IC50 of 0.14 ppm and Ag with IC50 of 0.70 ppm. Reduction in phenol degradation rate with increasing metal concentration is likely to be due to toxic effects on metabolic pathways, although not necessarily to population decline [72], as illustrated by the continued growth of strain AQ5-15 at increasing Cd concentration. Similarly, Ahmad et al. [40] reported that Arthrobacter bambusae strain AQ5-003 exhibited intolerance to 0.6 ppm of Ag with <50% phenol degradation and to a Cd concentration of 0.2 ppm with 20% phenol degradation. These heavy metals have cytotoxic effects on both structural and permeability properties of cell membranes and organelles, thereby inhibiting cellular enzymatic activities, in this case the enzyme system responsible for phenol degradation.

3.3.3. Binary Consortium

The tolerance of heavy metals by this binary consortium comprising strains AQ5-06 and AQ5-15 was identified by evaluating residual phenol concentration in culture media with 1.0 ppm of heavy metal (Figure 10). ANOVA confirmed that bacterial growth [F11,12 = 756.40, p < 0.0001] was significantly influenced by exposure to different metals. ANOVA could not be applied for phenol degradation because degradation by this consortium was not inhibited by 1.0 ppm Cu, Cr, Zn, Pb, As, Ni, Al, or Co, with 100% degradation of 0.5 g/L phenol within 48 h, whereas Ag, Hg and Cd completely inhibited degradation. The growth patterns differed with exposure to the different heavy metals, with the highest growth observed in the absence of heavy metals. Tukey’s multiple comparison tests confirmed that growth under control conditions (1.350 ± 0.0090) was significantly greater than with any of the metals tested. Growth was completely inhibited by Hg and was significantly lower than either control or other metals in the presence of Ag (0.240 ± 0.0200) and Cd (0.4140 ± 0.0055). This consortium’s inhibition pattern was similar to that described for strain AQ5-15, with Ag, Hg, and Cd being strong inhibitors of phenol degradation. Cd did not show an inhibitory effect on pure culture of strain AQ5-06; however, when combined with strain AQ5-15, Cd completely inhibited phenol degradation but not growth. Lu et al. [73] reported similar results, where the effect of metal ions on polychlorinated biphenyl (PCB) dechlorination by a binary mixture of Dehalococcoides mccartyi strain CG1 and Geobacter lovleyi strain LYY exhibited similar trends with respect to a pure culture of D. mccartyi strain CG1. These data suggest use of a mixed culture does not necessarily have a positive influence on tolerance of exposure to toxic heavy metals at high concentration.
Different concentrations of inhibiting heavy metals were added to media with 0.5 g/L phenol to determine the starting concentration of inhibition over a 48 h incubation (Figure 11). For Ag, ANOVA confirmed that both bacterial growth [F5,6 = 2296, p < 0.0001] and phenol degradation [F5,6 = 14477, p < 0.0001] were significantly influenced by metal concentration. Inhibition of phenol degradation by the mixed culture started at an Ag concentration of 0.2 ppm (48.64% inhibition) and further increase in concentration resulted in complete inhibition. Bacterial growth showed a similar response to increasing concentration. For Hg, ANOVA again confirmed that both bacterial growth [F6,7 = 17518, p < 0.0001] and phenol degradation [F6,7 = 45824, p < 0.0001] were significantly influenced by metal concentration. Inhibition of degradation started at a concentration of 0.5 ppm (58.25% inhibition). At a concentration of 0.6 ppm, inhibition reached 89.76% and further increase in concentration resulted in complete inhibition. For Cd, ANOVA also confirmed that both bacterial growth [F6,7 = 17086, p < 0.0001] and phenol degradation [F6,7 = 16423, p < 0.0001] were significantly influenced by metal concentration. Inhibition of degradation started at Cd concentration of 0.8 ppm (48.81% inhibition). At 0.9 ppm inhibition reached 89.76%. In this case, however, growth was strongly inhibited, having initially increased slightly when exposed to concentrations of up 0.4 ppm before decreasing with further increase in concentration.
The pure cultures of strains AQ5-15 and AQ5-06 could tolerate Ag concentrations up to 0.4 and 0.6 ppm, respectively, whereas the binary mixture could only tolerate up to 0.2 ppm, at which concentration phenol degradation was already reduced to half of the control level. This result contrasts with a number of studies reporting greater resistance of mixed microbial cultures compared with pure cultures towards toxic heavy metals [74,75]. However, this mixture showed increased tolerance to higher concentrations of Hg and Cd than did pure culture of strain AQ5-15, possibly suggesting that strain AQ5-06 may have a positive synergistic impact on its co-culture’s tolerance. IC50 of these heavy metals was determined from dose-response curves (Figure 12). Based on the modelled curve, Ag showed the strongest inhibition of phenol degradation with the lowest IC50 value of 0.15 ppm, followed by Hg with an IC50 of 0.48 ppm and Cd with an IC50 of 0.80 ppm.

4. Conclusions

This study utilized the advantage of statistical experimental design to optimize phenol biodegradation by a binary consortium of native Antarctic bacteria. The outcome sheds light on the potential application of RSM in the modeling and optimization of environmental factors in order to improve phenol degradation. Under optimized conditions, the mixed culture had better tolerance to a high concentration of phenol compared to pure culture. In terms of heavy metal tolerance, the binary consortium did not present any advantage over pure culture at high concentrations. Hence, further studies are needed to ensure the effectiveness of this binary consortium to bioremediate phenol pollution in the presence of different co-contaminants. The data from this study provides a solid foundation that will support the development of practical field bioremediation strategies, particularly in wastewater treatment systems in the Antarctic environment.

Author Contributions

Conceptualization, S.A.A., P.C., A.Z. and N.A.S.; methodology, A.Z., S.A.A., K.S. and K.A.K.; software, K.S. and K.A.K.; validation, K.S., A.Z., S.A.A., P.C., N.A.S. and K.A.K.; formal analysis, K.S., A.Z., S.A.A. and K.A.K.; investigation, K.S.; resources, S.A.A. and C.G.-F.; original draft preparation, K.S.; Writing—review and editing S.A.A., P.C., N.A.S., K.A.K., T.A.T.-M., C.G.-F. and A.Z.; supervision, S.A.A., P.C., N.A.S., K.A.K. and A.Z.; project administration, S.A.A., P.C., A.Z. and C.G.-F.; funding acquisition, A.Z., S.A.A. and C.G.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This project was financially supported by Shibaura Institute of Technology, Japan under Research Exchange Program Academic Year 2019 attached to K. Subramaniam, Matching Grant PUTRA (UPM-YPASM 9300430), PUTRA-IPS (9631800), PUTRA-Berimpak (9660000) disbursed by Universiti Putra Malaysia (UPM) and YPASM Berth Support by Sultan Mizan Antarctic Research Foundation (YPASM) fund under the research grant attached to S.A. Ahmad. P. Convey is supported by NERC core funding to the BAS ‘Biodiversity, Ecosystems and Adaptation’ Team. C. Gomez-Fuentes is supported by Centro de Investigacion y Monitoreo Ambiental Antàrctico (CIMAA) Project. K. Subramaniam is funded by a Graduate Research Fellowship (GRF) from Universiti Putra Malaysia.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Shibaura Institute of Technology, Universiti Putra Malaysia, Centro de Investigacion y Monitoreo Ambiental Antàrctico (CIMAA), Sultan Mizan Antarctic Research Foundation (YPASM) and National Antarctic Research Centre (NARC).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Al-Chalabi, M. The final frontier? An expedition to Antarctica and implications for the future. Crit. Arts 2017, 31, 110–118. [Google Scholar] [CrossRef]
  2. Brown, K.E.; King, C.K.; Harrison, P.L. Lethal and behavioral impacts of diesel and fuel oil on the Antarctic amphipod Paramoera walkeri. Environ. Toxicol. Chem. 2017, 36, 2444–2455. [Google Scholar] [CrossRef]
  3. Bargagli, R. Antarctic Ecosystems: Environmental Contamination, Climate Change, and Human Impact; Springer Science and Business Media: Berlin, Germany, 2005; Volume 175, pp. 125–290. [Google Scholar]
  4. Tin, T.; Fleming, Z.L.; Hughes, K.A.; Ainley, D.G.; Convey, P.; Moreno, C.A.; Pfeiffer, S.; Scott, J.; Snape, I. Impacts of local human activities on the Antarctic environment. Antarct. Sci. 2009, 21, 3–33. [Google Scholar] [CrossRef] [Green Version]
  5. Hughes, K.A. How committed are we to monitoring human impacts in Antarctica? Environ. Res. Lett. 2010, 5, 041001. [Google Scholar] [CrossRef]
  6. Kennicutt, M.C., II; Klein, A.; Montagna, P.; Sweet, S.; Wade, T.; Palmer, T.; Sericano, J.; Denoux, G. Temporal and spatial patterns of anthropogenic disturbance at McMurdo Station, Antarctica. Environ. Res. Lett. 2010, 5, 034010. [Google Scholar] [CrossRef]
  7. Stark, J.S.; Kim, S.L.; Oliver, J.S. Anthropogenic disturbance and biodiversity of marine benthic communities in Antarctica: A regional comparison. PLoS ONE 2014, 9, e98802. [Google Scholar] [CrossRef]
  8. Stark, J.S.; Mohammad, M.; McMinn, A.; Ingels, J. The effects of hydrocarbons on meiofauna in marine sediments in Antarctica. J. Exp. Mar. Biol. Ecol. 2017, 496, 56–73. [Google Scholar] [CrossRef]
  9. Vázquez, S.; Monien, P.; Minetti, R.P.; Jürgens, J.; Curtosi, A.; Primitz, J.V.; Frickenhaus, S.; Abele, D.; Mac Cormack, W.; Helmke, E. Bacterial communities and chemical parameters in soils and coastal sediments in response to diesel spills at Carlini Station, Antarctica. Sci. Total Environ. 2017, 605, 26–37. [Google Scholar] [CrossRef]
  10. Pi, Y.; Meng, L.; Bao, M.; Sun, P.; Lu, J. Degradation of crude oil and relationship with bacteria and enzymatic activities in laboratory testing. Int. Biodeterior. Biodegrad. 2016, 106, 106–116. [Google Scholar] [CrossRef]
  11. McWatters, R.S.; Wilkins, D.; Spedding, T.; Hince, G.; Raymond, B.; Lagerewskij, G.; Terry, D.; Wise, L.; Snape, I. On site remediation of a fuel spill and soil reuse in Antarctica. Sci. Total Environ. 2016, 571, 963–973. [Google Scholar] [CrossRef] [PubMed]
  12. Hui-Peng, L.; Jian, S.; Hua, Z.; Li, Z.; Yu-Tai, Q. Effects of phenols on the stability of FCC diesel fuel. Pet. Sci. Technol. 2009, 27, 486–497. [Google Scholar] [CrossRef]
  13. Singh, A.; Kumar, V.; Srivastava, J.N. Assessment of bioremediation of oil and phenol contents in refinery waste water via bacterial consortium. J. Pet. Environ. Biotechnol. 2013, 4, 1–4. [Google Scholar] [CrossRef] [Green Version]
  14. Allinson, M.; Kadokami, K.; Shiraishi, F.; Nakajima, D.; Zhang, J.; Knight, A.; Gray, S.R.; Scales, P.J.; Allinson, G. Wastewater recycling in Antarctica: Performance assessment of an advanced water treatment plant in removing trace organic chemicals. J. Environ. Manag. 2018, 224, 122–129. [Google Scholar] [CrossRef] [PubMed]
  15. Kwon, K.D.; Jo, W.K.; Lim, H.J.; Jeong, W.S. Volatile pollutants emitted from selected liquid household products. Environ. Sci. Pollut. Res. 2008, 15, 521–526. [Google Scholar] [CrossRef]
  16. Vanden Eynde, J.J. How efficient is my (medicinal) chemistry? Pharmaceuticals 2016, 9, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Vecchiato, M.; Argiriadis, E.; Zambon, S.; Barbante, C.; Toscano, G.; Gambaro, A.; Piazza, R. Persistent Organic Pollutants (POPs) in Antarctica: Occurrence in continental and coastal surface snow. Microchem. J. 2015, 119, 75–82. [Google Scholar] [CrossRef]
  18. Zangrando, R.; Barbaro, E.; Vecchiato, M.; Kehrwald, N.M.; Barbante, C.; Gambaro, A. Levoglucosan and phenols in Antarctic marine, coastal and plateau aerosols. Sci. Total Environ. 2016, 544, 606–616. [Google Scholar] [CrossRef] [PubMed]
  19. Zangrando, R.; Corami, F.; Barbaro, E.; Grosso, A.; Barbante, C.; Turetta, C.; Capodaglio, G.; Gambaro, A. Free phenolic compounds in waters of the Ross Sea. Sci. Total Environ. 2019, 650, 2117–2128. [Google Scholar] [CrossRef]
  20. Mohammadi, S.; Kargari, A.; Sanaeepur, H.; Abbassian, K.; Najafi, A.; Mofarrah, E. Phenol removal from industrial wastewaters: A short review. Desalin. Water Treat. 2014, 53, 2215–2234. [Google Scholar] [CrossRef]
  21. Tengku-Mazuki, T.A.; Subramanian, K.; Zakaria, N.N.; Convey, P.; Khalil, K.A.; Lee, G.L.Y.; Zulkharnain, A.; Shaharuddin, N.A.; Ahmad, S.A. Optimization of phenol degradation by Antarctic bacterium Rhodococcus sp. Antarct. Sci. 2020, 32, 486–495. [Google Scholar] [CrossRef]
  22. Camenzuli, D.; Freidman, B.L. On-site and in situ remediation technologies applicable to petroleum hydrocarbon contaminated sites in the Antarctic and Arctic. Polar Res. 2015, 34, 24492. [Google Scholar] [CrossRef] [Green Version]
  23. Nakamura, Y.; Sawada, T. Biodegradation of phenol in the presence of heavy metals. J. Chem. Technol. Biotechnol. 2000, 75, 137–142. [Google Scholar] [CrossRef]
  24. Zakaria, N.N.; Ahmad, S.A.; Yin, G.L.L.; Yasid, N.A.; Motharasan, M.; Subramaniam, K.; Mazuki, T.A.T.; Nawawi, N.M.; Shukor, M.Y. Biodegradation of phenol by Antarctic bacterium Rhodococcus baikonurensis strain AQ5-001 in the presence of heavy metals. Malaysian J. Biochem. Mol. Biol. 2018, 21, 29–36. [Google Scholar]
  25. Tengku-Mazuki, T.A.; Zulkharnain, A.; Subramaniam, K.; Convey, P.; Gomez-Fuentes, C.; Ahmad, S.A. Effects of zinc (Zn) and chromium (Cr) on the phenol-degrading bacteria growth kinetics. Malays. J. Biochem. Mol. Biol. 2020, 23, 1–4. [Google Scholar]
  26. Duraisamy, P.; Sekar, J.; Arunkumar, A.D.; Ramalingam, P.V. Kinetics of phenol biodegradation by heavy metal tolerant rhizobacteria Glutamicibacter nicotianae MSSRFPD35 from distillery effluent contaminated soils. Front. Microbiol. 2020, 11, 1573. [Google Scholar] [CrossRef]
  27. Stark, J.S.; Bridgen, P.; Dunshea, G.; Galton-Fenzi, B.; Hunter, J.; Johnstone, G.; King, C.; Leeming, R.; Palmer, A.; Smith, J.; et al. Dispersal and dilution of wastewater from an ocean outfall at Davis Station, Antarctica, and resulting environmental contamination. Chemosphere 2016, 152, 142–157. [Google Scholar] [CrossRef] [PubMed]
  28. Chu, W.L.; Dang, N.L.; Kok, Y.Y.; Yap, K.S.I.; Phang, S.M.; Convey, P. Heavy metal pollution in Antarctica and its potential impacts on algae. Polar Sci. 2019, 20, 75–83. [Google Scholar] [CrossRef]
  29. Wang, Z.; Zhang, J.; Zhang, Y.; Hesham, A.L.; Yang, M. Molecular characterization of a bacterial consortium enriched from an oilfield that degrades phenanthrene. Biotechnol. Lett. 2006, 28, 617–621. [Google Scholar] [CrossRef] [Green Version]
  30. Bhattacharya, M.; Guchhait, S.; Biswas, D.; Datta, S. Waste lubricating oil removal in a batch reactor by mixed bacterial consortium: A kinetic study. Bioprocess Biosyst. Eng. 2015, 38, 2095–2106. [Google Scholar] [CrossRef] [PubMed]
  31. Sivasubramanian, S.; Namasivayam, S.K.R. Phenol degradation studies using microbial consortium isolated from environmental sources. J. Environ. Chem. Eng. 2015, 3, 243–252. [Google Scholar] [CrossRef]
  32. Roslee, A.F.A.; Zakaria, N.N.; Convey, P.; Zulkharnain, A.; Lee, G.L.Y.; Gomez-Fuentes, C.; Ahmad, S.A. Statistical optimisation of growth conditions and diesel degradation by the Antarctic bacterium, Rhodococcus sp. strain AQ5–07. Extremophiles 2020, 24, 277–291. [Google Scholar] [CrossRef]
  33. Abdulrasheed, M.; Zulkharnain, A.; Zakaria, N.N.; Roslee, A.F.A.; Abdul Khalil, K.; Napis, S.; Convey, P.; Gomez-Fuentes, C.; Ahmad, S.A. Response surface methodology optimization and kinetics of diesel degradation by a cold-adapted Antarctic bacterium, Arthrobacter sp. strain AQ5-05. Sustainability 2020, 12, 6966. [Google Scholar] [CrossRef]
  34. Ungureanu, C.V.; Favier, L.; Bahrim, G.E. Improving Biodegradation of Clofibric Acid by Trametes pubescens through the Design of Experimental Tools. Microorganisms 2020, 8, 1243. [Google Scholar] [CrossRef]
  35. Lee, G.L.Y.; Ahmad, S.A.; Yasid, N.A.; Zulkharnain, A.; Convey, P.; Johari, W.L.W.; Alias, S.A.; Gonzalez-Rocha, G.; Shukor, M.Y. Biodegradation of phenol by cold-adapted bacteria from Antarctic soils. Polar Biol. 2018, 41, 553–562. [Google Scholar] [CrossRef]
  36. Subramaniam, K.; Mazuki, T.A.T.; Shukor, M.Y.; Ahmad, S.A. Isolation and optimisation of phenol degradation by Antarctic isolate using one factor at time. Malays. J. Biochem. Mol. Biol. 2019, 22, 79–86. [Google Scholar]
  37. Abdulrasheed, M.; Zakaria, N.N.; Roslee, A.F.A.; Shukor, M.Y.; Zulkharnain, A.; Napis, S.; Convey, P.; Alias, S.A.; Gonzalez-Rocha, G.; Ahmad, S.A. Biodegradation of diesel oil by cold-adapted bacterial strains of Arthrobacter spp. from Antarctica. Antarct. Sci. 2020, 32, 341–353. [Google Scholar] [CrossRef]
  38. APHA (American Public Health Association). Standard Method for the Examination of Water and Wastewater, 20th ed.; American Public Health Association: Washington, DC, USA, 2005; pp. 540–544. [Google Scholar]
  39. Suhaila, Y.N.; Ramanan, R.N.; Rosfarizan, M.; Latif, I.A.; Ariff, A.B. Optimization of parameters for improvement of phenol degradation by Rhodococcus UKMP-5M using response surface methodology. Ann. Microbiol. 2013, 63, 513–521. [Google Scholar] [CrossRef]
  40. Ahmad, S.A.; Asokan, G.; Yasid, N.A.; Nawawi, N.M.; Subramaniam, K.; Zakaria, N.N.; Shukor, M.Y. Effect of heavy metals on biodegradation of phenol by Antarctic bacterium: Arthrobacter bambusae strain AQ5-003. Malaysian J. Biochem. Mol. Biol. 2018, 21, 47–51. [Google Scholar]
  41. Carlson, H.K.; Price, M.N.; Callaghan, M.; Aaring, A.; Chakraborty, R.; Liu, H.; Kuehl, J.V.; Arkin, A.P.; Deutschbauer, A.M. The selective pressures on the microbial community in a metal-contaminated aquifer. ISME J. 2019, 13, 937–949. [Google Scholar] [CrossRef] [Green Version]
  42. Tecon, R.; Or, D. Cooperation in carbon source degradation shapes spatial self-organization of microbial consortia on hydrated surfaces. Sci. Rep. 2017, 7, 1–11. [Google Scholar] [CrossRef] [Green Version]
  43. Lee, G.L.Y. Biodegradation of Phenol by Cold-Adapted Bacteria Isolated from Antarctic Soils. Ph.D. Thesis, Universiti Putra Malaysia, Selangor, Malaysia, 2018. [Google Scholar]
  44. Subramaniam, K.; Shaharuddin, N.A.; Tengku-Mazuki, T.A.; Zulkharnain, A.; Khalil, K.A.; Convey, P.; Ahmad, S.A. Statistical optimisation for enhancement of phenol biodegradation by the Antarctic soil bacterium Arthrobacter sp. strain AQ5-15 using response surface methodology. J. Environ. Biol. 2020, 41, 1560–1569. [Google Scholar] [CrossRef]
  45. Clarke, K.G. Bioprocess Engineering: An Introductory Engineering and Life Science Approach. In Microbiology; Woodhead Publishing Limited: Cambridge, UK, 2013; pp. 7–24. [Google Scholar]
  46. Varjani, S.J.; Upasani, V.N. A new look on factors affecting microbial degradation of petroleum hydrocarbon pollutants. Int. Biodeterior. Biodegrad. 2017, 120, 71–83. [Google Scholar] [CrossRef]
  47. Lin, C.; Gan, L.; Chen, Z.L. Biodegradation of naphthalene by strain Bacillus fusiformis (BFN). J. Hazard. Mater. 2010, 182, 771–777. [Google Scholar] [CrossRef] [PubMed]
  48. Ucun, H.; Yildiz, E.; Nuhoglu, A. Phenol biodegradation in a batch jet loop bioreactor (JLB): Kinetics study and pH variation. Bioresour. Technol. 2010, 101, 2965–2971. [Google Scholar] [CrossRef] [PubMed]
  49. Turner, J.; Lu, H.; King, J.; Marshall, G.J.; Phillips, T.; Bannister, D.; Colwell, S. Extreme temperatures in the Antarctic. J. Clim. 2021, 34, 2653–2668. [Google Scholar] [CrossRef]
  50. Tay, J.H.; Jiang, H.L.; Tay, S.T.L. High-rate biodegradation of phenol by aerobically grown microbial granules. Int. J. Environ. Eng. 2004, 130, 1415–1423. [Google Scholar] [CrossRef]
  51. Tay, S.T.L.; Moy, B.Y.P.; Maszenan, A.M.; Tay, J.H. Comparing activated sludge and aerobic granules as microbial inocula for phenol biodegradation. Appl. Microbiol. Biotechnol. 2005, 67, 708–713. [Google Scholar] [CrossRef] [PubMed]
  52. Wen, Y.; Li, C.; Song, X.; Yang, Y. Biodegradation of phenol by Rhodococcus sp. strain SKC: Characterization and kinetics study. Molecules 2020, 25, 3665. [Google Scholar] [CrossRef] [PubMed]
  53. Senthilvelan, T.; Kanagaraj, J.; Panda, R.C.; Mandal, A.B. Biodegradation of phenol by mixed microbial culture: An eco-friendly approach for the pollution reduction. Clean Technol. Environ. Policy 2014, 16, 113–126. [Google Scholar] [CrossRef]
  54. Hamitouche, A.; Amrane, A.; Bendjama, Z.; Kaouah, F. Effect of the ammonium chloride concentration on the mineral medium composition—Biodegradation of phenol by a microbial consortium. Int. J. Environ. Res. 2010, 4, 849–854. [Google Scholar]
  55. Bhatia, S.K.; Bhatia, R.K.; Choi, Y.K.; Kan, E.; Kim, Y.G.; Yang, Y.H. Biotechnological potential of microbial consortia and future perspectives. Crit. Rev. Biotechnol. 2018, 38, 1209–1229. [Google Scholar] [CrossRef] [PubMed]
  56. Deeb, B.E.; Altalhi, A.D. Degradative plasmid and heavy metal resistance plasmid naturally coexist in phenol and cyanide assimilating bacteria. Am. J. Biochem. Biotechnol. 2009, 5, 84–93. [Google Scholar] [CrossRef]
  57. Al-Defiery, M.E.; Reddy, G. Influence of metal ions concentration on phenol degradation by Rhodococcus pyridinivorans GM3. Mesop. Environ. J. 2014, 1, 30–38. [Google Scholar]
  58. Trevizani, T.H.; Petti, M.A.V.; Ribeiro, A.P.; Corbisier, T.N.; Figueira, R.C.L. Heavy metal concentrations in the benthic trophic web of martel inlet, admiralty Bay (King George Island, Antarctica). Mar. Pollut. Bull. 2018, 130, 198–205. [Google Scholar] [CrossRef] [PubMed]
  59. Chu, Z.; Yang, Z.; Wang, Y.; Sun, L.; Yang, W.; Yang, L.; Gao, Y. Assessment of heavy metal contamination from penguins and anthropogenic activities on Fildes peninsula and Ardley island, Antarctic. Sci. Total Environ. 2019, 646, 951–957. [Google Scholar] [CrossRef] [PubMed]
  60. Lischka, A.; Bustamante, P.; Braid, H.; Piatkowski, U.; Lacoue-Labarthe, T. Trophic ecology drives trace element concentrations in the Antarctic octopod community. Sci. Total Environ. 2021, 768, 144373. [Google Scholar] [CrossRef] [PubMed]
  61. Abdulrasheed, M.; Roslee, A.F.; Zakaria, N.N.; Zulkharnain, A.; Lee, G.L.Y.; Convey, P.; Napis, S.; Ahmad, S.A. Effects of heavy metals on diesel metabolism of psychrotolerant strains of Arthrobacter sp. from Antarctica. J. Environ. Biol. 2020, 41, 966–972. [Google Scholar] [CrossRef]
  62. Ibrahim, S.; Zulkharnain, A.; Zahri, K.N.M.; Lee, G.L.Y.; Convey, P.; Gomez Fuentes, C.; Sabri, S.; Khalil, K.A.; Alias, S.A.; Gonzales-Rocha, G.; et al. Effect of heavy metals and other xenobiotics on biodegradation of waste canola oil by cold-adapted Rhodococcus sp. AQ5-07. Rev. Mex. Ing. Quim. 2020, 19, 1041–1052. [Google Scholar] [CrossRef] [Green Version]
  63. Zahri, K.N.M.; Zulkharnain, A.; Gomez-Fuentes, C.; Sabri, S.; Ahmad, S.A. Effects of heavy metals on Antarctic bacterial cell growth kinetics and degradation of waste canola oil. J. Environ. Biol. 2020, 41, 1433–1441. [Google Scholar] [CrossRef]
  64. Zakaria, N.N.; Roslee, A.F.A.; Gomez-Fuentes, C.; Zulkharnain, A.; Abdulrasheed, M.; Sabri, S.; Ramirez-Moreno, N.; Calisto-Ulloa, N.; Ahmad, S.A. Kinetic studies of marine psychrotolerant microorganisms capable of degrading diesel in the presence of heavy metals. Rev. Mex. Ing. Quim. 2020, 19, 1375–1388. [Google Scholar] [CrossRef]
  65. Ladomersky, E.; Petris, M.J. Copper tolerance and virulence in bacteria. Metallomics 2015, 7, 957–964. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Issazadeh, K.; Jahanpour, N.; Pourghorbanali, F.; Raeisi, G.; Faekhondeh, J. Heavy metals resistance by bacterial strains. Ann. Biol. Res. 2013, 4, 60–63. [Google Scholar]
  67. Singh, R.; Gautam, N.; Mishra, A.; Gupta, R. Heavy metals and living systems: An overview. Indian J. Pharmacol. 2011, 43, 246. [Google Scholar] [CrossRef] [Green Version]
  68. Yoo, J.Y.; Choi, J.; Lee, T.; Park, J.W. Organobentonite for sorption and degradation of phenol in the presence of heavy metals. Water Air Soil Pollut. 2004, 154, 225–237. [Google Scholar] [CrossRef]
  69. Hrynkiewicz, K.; Baum, C. Application of Microorganisms in Bioremediation of Environment from Heavy Metals. In Environmental Deterioration and Human Health; Malik, A., Grohmann, E., Akhtar, R., Eds.; Springer: Dordrecht, The Netherlands, 2014; pp. 215–227. [Google Scholar]
  70. Bruins, M.R.; Kapil, S.; Oehme, F.W. Microbial resistance to metals in the environment. Ecotoxicol. Environ. Saf. 2000, 45, 198–207. [Google Scholar] [CrossRef] [PubMed]
  71. Schink, S.J.; Biselli, E.; Ammar, C.; Gerland, U. Death rate of E. coli during starvation is set by maintenance cost and biomass recycling. Cell Syst. 2019, 9, 64–73. [Google Scholar] [CrossRef]
  72. Al-Saleh, E.S.; Obuekwe, C. Inhibition of hydrocarbon bioremediation by lead in a crude oil-contaminated soil. Int. Biodeterior. Biodegrad. 2005, 56, 1–7. [Google Scholar] [CrossRef]
  73. Lu, Q.; Zou, X.; Liu, J.; Liang, Z.; Shim, H.; Qiu, R.; Wang, S. Inhibitory effects of metal ions on reductive dechlorination of polychlorinated biphenyls and perchloroethene in distinct organohalide-respiring bacteria. Environ. Int. 2020, 135, 105373. [Google Scholar] [CrossRef] [PubMed]
  74. Kang, C.H.; Kwon, Y.J.; So, J.S. Bioremediation of heavy metals by using bacterial mixtures. Ecol. Eng. 2016, 89, 64–69. [Google Scholar] [CrossRef]
  75. Oaikhena, E.E.; Makaije, D.B.; Denwe, S.D.; Namadi, M.M.; Haroun, A.A. Bioremediation potentials of heavy metal tolerant bacteria isolated from petroleum refinery effluent. Am. J. Environ. Prot. 2016, 5, 29–34. [Google Scholar] [CrossRef] [Green Version]
Diversity 13 00643 g001 550
Figure 1. Diagnostic plots of the central composite design model of phenol degradation by the binary consortium. (a) Similarity plot between predicted and actual values for phenol degradation; (b) Normal probability plot against residuals for phenol degradation.
Figure 1. Diagnostic plots of the central composite design model of phenol degradation by the binary consortium. (a) Similarity plot between predicted and actual values for phenol degradation; (b) Normal probability plot against residuals for phenol degradation.
Diversity 13 00643 g001
Diversity 13 00643 g002 550
Figure 2. The one-factor plot showing the significant effect of temperature on phenol degradation by the binary consortium.
Figure 2. The one-factor plot showing the significant effect of temperature on phenol degradation by the binary consortium.
Diversity 13 00643 g002
Diversity 13 00643 g003 550
Figure 3. (a) Bacterial growth and (b) degradation of different initial concentrations of phenol by the binary consortium under optimized conditions (0.40 g/L (NH4)2SO4, 0.13 g/L NaCl, pH 7.25, 12.50°C). The control (cross mark) shows the initial phenol concentration in the absence of the bacterial strain. Error bars represent the mean ± standard deviation of three replicates.
Figure 3. (a) Bacterial growth and (b) degradation of different initial concentrations of phenol by the binary consortium under optimized conditions (0.40 g/L (NH4)2SO4, 0.13 g/L NaCl, pH 7.25, 12.50°C). The control (cross mark) shows the initial phenol concentration in the absence of the bacterial strain. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g003
Diversity 13 00643 g004 550
Figure 4. Phenol degradation and growth of Arthrobacter sp. strain AQ5-06 in the presence of 1.0 ppm concentrations of different heavy metals. Error bars represent the mean ± standard deviation of three replicates.
Figure 4. Phenol degradation and growth of Arthrobacter sp. strain AQ5-06 in the presence of 1.0 ppm concentrations of different heavy metals. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g004
Diversity 13 00643 g005 550
Figure 5. Effects of exposure to different concentrations of Ag and Hg on phenol degradation (solid line) and growth (dotted line) of Arthrobacter sp. strain AQ5-06. Error bars represent the mean ± standard deviation of three replicates.
Figure 5. Effects of exposure to different concentrations of Ag and Hg on phenol degradation (solid line) and growth (dotted line) of Arthrobacter sp. strain AQ5-06. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g005
Diversity 13 00643 g006 550
Figure 6. Dose-response curves identifying IC50 values for Arthrobacter sp. strain AQ5-06 phenol degradation after 120-h exposure to different concentrations of (a) Ag and (b) Hg.
Figure 6. Dose-response curves identifying IC50 values for Arthrobacter sp. strain AQ5-06 phenol degradation after 120-h exposure to different concentrations of (a) Ag and (b) Hg.
Diversity 13 00643 g006
Diversity 13 00643 g007 550
Figure 7. Phenol degradation and growth of Arthrobacter sp. strain AQ5-15 in the presence of 1.0 ppm concentrations of different heavy metals. Error bars represent the mean ± standard deviation of three replicates.
Figure 7. Phenol degradation and growth of Arthrobacter sp. strain AQ5-15 in the presence of 1.0 ppm concentrations of different heavy metals. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g007
Diversity 13 00643 g008 550
Figure 8. Effects of different concentrations of Ag, Hg, and Cd on phenol degradation (solid line) and growth (dotted line) of Arthrobacter sp. strain AQ5-15. Error bars represent the mean ± standard deviation of three replicates.
Figure 8. Effects of different concentrations of Ag, Hg, and Cd on phenol degradation (solid line) and growth (dotted line) of Arthrobacter sp. strain AQ5-15. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g008
Diversity 13 00643 g009 550
Figure 9. Dose-response curves indicating IC50 values for Arthrobacter sp. strain AQ5-15 phenol degradation after 96-h exposure to different concentrations of (a) Ag, (b) Hg, and (c) Cd.
Figure 9. Dose-response curves indicating IC50 values for Arthrobacter sp. strain AQ5-15 phenol degradation after 96-h exposure to different concentrations of (a) Ag, (b) Hg, and (c) Cd.
Diversity 13 00643 g009
Diversity 13 00643 g010 550
Figure 10. Effects of 1.0 ppm concentration of different heavy metals on phenol degradation and growth on the binary consortium. Error bars represent the mean ± standard deviation of three replicates.
Figure 10. Effects of 1.0 ppm concentration of different heavy metals on phenol degradation and growth on the binary consortium. Error bars represent the mean ± standard deviation of three replicates.
Diversity 13 00643 g010
Diversity 13 00643 g011 550
Figure 11. The influence of exposure to different concentrations of Ag, Hg and Cd on phenol degradation (solid line) and the growth (dotted line) of the binary consortium.
Figure 11. The influence of exposure to different concentrations of Ag, Hg and Cd on phenol degradation (solid line) and the growth (dotted line) of the binary consortium.
Diversity 13 00643 g011
Diversity 13 00643 g012 550
Figure 12. Dose-response curves indicating IC50 values for the binary consortium phenol degradation after 48-h exposure to different concentrations of (a) Ag, (b) Hg, and (c) Cd.
Figure 12. Dose-response curves indicating IC50 values for the binary consortium phenol degradation after 48-h exposure to different concentrations of (a) Ag, (b) Hg, and (c) Cd.
Diversity 13 00643 g012
Table
Table 1. Experimental ranges of four different variables tested in CCD.
Table 1. Experimental ranges of four different variables tested in CCD.
VariablesSymbolExperimental Values
−2−10+1+2
(NH4)2SO4 concentration (g/L)A0.20.30.40.50.6
NaCl concentration (g/L)B0.080.100.130.150.17
pHC6.757.007.257.507.75
Temperature (°C)D7.010.012.515.017.50
Table
Table 2. Time taken for binary consortium with different inoculum ratios to completely degrade 0.5 g/L phenol at 10 °C.
Table 2. Time taken for binary consortium with different inoculum ratios to completely degrade 0.5 g/L phenol at 10 °C.
DMCInoculum RatioGrowth Rate (h−1)Incubation Time (h)
AQ5-06 + AQ5-151:10.0191 (±0.0263)48
1:30.0106 (±0.0017)84
3:10.0121 (±0.0227)84
Table
Table 3. Analysis of variance (ANOVA) of phenol degradation by the binary consortium.
Table 3. Analysis of variance (ANOVA) of phenol degradation by the binary consortium.
SourceSum of SquaresDFMean SquareF-ValueProb > F
Model6879.318859.9137.51<0.0001 ***
A0.0110.0125.3 × 10−30.9819
B51.92151.922.260.1472
C38.35138.351.670.2099
D1581.1311581.1368.97<0.0001 ***
A2544.171544.1723.74<0.0001 ***
B2598.611598.6126.11<0.0001 ***
C2577.341577.3425.18<0.0001 ***
D24718.1014718.10205.80<0.0001 ***
Residual481.432122.93
Lack of Fit378.551623.661.150.4773
Pure Error102.88520.58
Cor Total7360.7429
Standard deviation4.79R20.9346
Mean73.31Adjusted R20.9097
Coefficient variance6.53Predicted R20.7573
PRESS1786.11Adequate Precision26.1944
A: Ammonium sulphate concentration (g/L); B: Sodium chloride concentration (g/L); C: pH; D: Temperature (°C). *** p < 0.001.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Subramaniam, K.; Ahmad, S.A.; Convey, P.; Shaharuddin, N.A.; Khalil, K.A.; Tengku-Mazuki, T.A.; Gomez-Fuentes, C.; Zulkharnain, A. Statistical Assessment of Phenol Biodegradation by a Metal-Tolerant Binary Consortium of Indigenous Antarctic Bacteria. Diversity 2021, 13, 643. https://doi.org/10.3390/d13120643

AMA Style

Subramaniam K, Ahmad SA, Convey P, Shaharuddin NA, Khalil KA, Tengku-Mazuki TA, Gomez-Fuentes C, Zulkharnain A. Statistical Assessment of Phenol Biodegradation by a Metal-Tolerant Binary Consortium of Indigenous Antarctic Bacteria. Diversity. 2021; 13(12):643. https://doi.org/10.3390/d13120643

Chicago/Turabian Style

Subramaniam, Kavilasni, Siti Aqlima Ahmad, Peter Convey, Noor Azmi Shaharuddin, Khalilah Abdul Khalil, Tengku Athirrah Tengku-Mazuki, Claudio Gomez-Fuentes, and Azham Zulkharnain. 2021. "Statistical Assessment of Phenol Biodegradation by a Metal-Tolerant Binary Consortium of Indigenous Antarctic Bacteria" Diversity 13, no. 12: 643. https://doi.org/10.3390/d13120643

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop