Multibiomarker approach in fish to assess a heavily polluted Brazilian estuary, Guanabara Bay

Brazil’s Guanabara Bay (GB), located in Rio De Janeiro, is a deeply contaminated, eutrophic waterbody that challenges the understanding of the effects of pollutants on the biota. This paper presents a strategy to evaluate the impact of contamination utilizing a multibiomarker approach in two fish species: corvine (Micropogonias furnieri) and burrfish (Chilomycterus spinosus). The strategy is comprised of a general biomarker of fish’ physical condition, the condition factor (CF), and specific biomarkers of pollutant exposure such as acetylcholinesterase (AChE), metallothionein (MT) activity and biliary polycyclic aromatic hydrocarbons (PAH) metabolites. Our results indicate that fish from GB are greatly affected by environmental pollution. CF values were lower in fishes from GB than in the reference site indicating that these fishes were under higher environmental stress. Lower AChE activity level in both species showed vulnerability to the presence of pesticide residues. Higher levels of MT in both species in GB reflect the consequences of heavy metal exposure in the bay, in spite of their bioavailability being restricted specially by the high organic matter content of GB. The levels of PAHs were higher in GB for both fish species, indicating exposure to these substances. However, the fish species showed different behavior regarding the origin of the PAHs. The multibiomarker approach used in this study evidently depicted effects on the health of fish in a waterbody with a complex polluted situation and further categorized the effects of anthropogenic activities in this aquatic system.

of a general biomarker of fish' physical condition, the condition factor (CF), and specific biomarkers of pollutant exposure such as acetylcholinesterase (AChE), metallothionein (MT) activity and biliary polycyclic aromatic hydrocarbons (PAH) metabolites.
Our results indicate that fish from GB are greatly affected by environmental pollution. CF values were lower in fishes from GB than in the reference site indicating that these fishes were under higher environmental stress. Lower AChE activity level in both species showed vulnerability to the presence of pesticide residues. Higher levels of MT in both species in GB reflect the consequences of heavy metal exposure in the bay, in spite of their bioavailability being restricted specially by the high organic matter content of GB. The levels of PAHs were higher in GB for both Abstract Brazil's Guanabara Bay (GB), located in Rio De Janeiro, is a deeply contaminated, eutrophic waterbody that challenges the understanding of the effects of pollutants on the biota. This paper presents a strategy to evaluate the impact of contamination utilizing a multibiomarker approach in two fish species: corvine (Micropogonias furnieri) and burrfish (Chilomycterus spinosus). The strategy is comprised Introduction Guanabara Bay, GB, the largest metropolitan estuary on the Brazilian coast, is substantially deteriorated by anthropogenic activities. It receives contaminated discharges from about 6000 industries, two harbors, two airports, and 15 oil terminals situated in its surrounding area (INEA-Instituto Estadual do Ambiente, 2019; Fries et al., 2019), which is the second major industrial district in Brazil. In addition, the sewage of a population of over 11 million residents is also discharged into the estuary through approximately 45 rivers (INEA-Instituto Estadual do Ambiente, 2019). The degradation of GB has been an object of much research in recent years (Fries et al., 2019;Silveira et al., 2017;Neto et al., 2017;Abreu et al., 2016;de Assis Costa et al., 2018). However, the real impact that such pollution has on the environmental quality of the estuary remains unclear. It is therefore highly pertinent to carry out assessments that examine the deleterious effects this situation has on the ecosystem (Fries et al., 2019).
A mixture of eutrophication and high concentration of organic carbon was reported in several studies (Abreu et al., 2016;Fries et al., 2019;Olivatto et al., 2019). This complicated combination, paired with the lack of knowledge regarding the interactions between pollutants and their effects, and the presence of a reducing environment makes the analysis challenging (Fistarol et al., 2015;Soares-Gomes et al., 2016). Furthermore, there are a limited number of reports that tackle the current situation comprehensively, analyzing the multiplicity of indicators and their effect on different organisms.
A biomarker is defined as a "quantitative measure of changes in molecular or cellular components, processes, structures and functions related to exposure to environmental chemicals" (Dalzochio et al., 2016). They indicate an early response to pollutants and are normally specific to a certain type of contaminants (Hook et al., 2014). Biomarkers are progressively internationally recognized instruments for the evaluation of contamination impacts, and many are now integrated in environmental assessment plans in several countries (Trapp et al., 2014;Viarengo et al., 2007). Biomarkers have been classified into classes that indicate exposure to environmental contaminants or harmful health effects from pollutant exposures (Hook et al., 2014;Van der Oost et al., 2003;Viarengo et al., 2007).
In this regard, the utilization of biomarkers has shown to be a sufficiently sensitive instrument to measure biological effects and, therefore, assess environmental quality (Martinez-Haro et al., 2015;Milinkovitch et al., 2019) and are relatively effective in revealing the overall toxicities of complex mixtures (Linde-Arias et al., 2008;Martinez-Haro et al., 2015). The concomitant utilization of various biomarkers is key in minimizing potential misunderstanding that arises in complex conditions of contamination (Dalzochio et al., 2016).
In the present study, we use a multibiomarker approach that examines specific biomarkers relating to contaminant exposure such as metallothionein (MT) and acetylcholinesterase (AChE) activity, biliary PAH metabolites, as well as a general biomarker indicating the physical condition of fish, the condition factor (CF).
The condition factor (CF) value measures the condition of a fish's whole body, and it was chosen as an informative source on the potential impacts of pollution. Although this parameter may be influenced by non-contaminant causes such as physiological and nutritional status, disease, level, and season among others, it can be utilized as a primary screening biomarker to show effects of exposure or to inform on energy reserves (Moreira Freire et al., 2020;Van der Oost et al., 2003).
AChE activity was selected as a biomarker of exposure to pesticides, as its inhibition is clearly connected with exposure to pesticides extensively utilized in agriculture (Andreescu & Marty, 2006). It can be utilized to identify carbamate and organophosphorus pesticide exposure in fish (Ballesteros et al., 2017;Umar & Aisami, 2020) and has consequently been broadly used in aquatic pollution assessment (El-Nahhal, 2018;Pan et al., 2018;Zurita et al., 2019).
MT was chosen to be a biomarker of exposure to metals. As MT levels can be elevated through induction by metal ions, many studies have used MT concentration as a biomarker to evaluate the effects of aquatic contamination by metals in aquatic organisms (Delahaut et al., 2019;Fabrin et al., 2018;Linde et al., 1999Linde et al., , 2001Linde-Arias et al., 2008).
Polycyclic aromatic hydrocarbons (PAHs) are present in the environment mainly due to human activities (EC -European Commission, 2014;Abdel-Shafy & Mansour, 2016) and have been connected to teratogenic effects in living organisms (Santos et al., 2019). Determining metabolite levels in bile as fluorescent aromatic compounds (FAC) has shown to be a reliable procedure of assessing and identifying fish exposure to PAH (Freire et al., 2021;Kammann et al., 2017;Vaaland et al., 2020).
Fish are regarded as the most suitable organisms for evaluating the consequences of contamination in water bodies because they exert a significant ecological function in aquatic food webs given their role as carriers of energy along the trophic levels ( Van der Oost et al., 2003). Different species of fish will react differently to pollutants based on their natural habitat, their metabolism, and their life cycle.
An adequate selection of fish species is crucial for a correct analysis of the effects that pollutants have on the biota as well as its possible effects on humans. Given that different species behave differently towards the same molecules, it is important to select more than one species to comprehend the outcome of the pollutants in the environment.
In light of that, the use of more than one species to validate the studies is recommended. In the present study, we have selected two predatory fish species: corvine (Micropogonias furnieri) and burrfish (Chilomycterus spinosus). Both these fish are demersal subtropical, causing them to be susceptible to the pollutant conditions that are prominent in GB. Furthermore, corvine possess great economic importance, as they are one of the main species landed by local fishermen within the bay (Soares-Gomes et al., 2016), while the burrfish has a high ecological relevance. Both fish species reflect wide geographical distribution and the physiological and ecological features necessary to be utilized as a sentinel species (Blasco et al., 2016;Holt & Miller, 2011).
The research uses a multibiomarker approach in two fish species to identify the physiological damage that specific pollutants are causing. Examining the biological responses through such an integrated strategy allows us to assess the connections between biomarkers of the most predominant aquatic contaminants.

Material and methods
Study area GB (see Fig. 1) is situated between 22°40′ and 23°00′S latitude and 43°00′-43°18′W longitude and covers an area of 346 km 2 (INEA-Instituto Estadual do Ambiente, 2019). It is the largest metropolitan river mouth on the Brazilian coast, severely deteriorated by human habitation, and one of the most populated urban regions of the world, the 20th (UN, 2016). Four municipalities, Rio de Janeiro, Niteroi, Duque de Caxias, and São Gonçalo, are situated in the area with a total population of approximately 11 million (IBGE, 2021). GB ( Fig. 1) was sampled in a broad area distributed within the Bay.
Itaipu Beach (IB) is located outside the bay, facing the Atlantic Ocean ( Fig. 1). Although it is considered a well conserved area, increasing urbanization due to tourism has affected its chemical, physical, and biological characteristics (Barbosa & Begossi, 2004), and it also has similar geochemical characteristics as the GB. Therefore, it was chosen as a reference site. Samples from IB would be used as controls in order to measure the biological responses to pollution condition in the water and to explore site-specific connections between biomarkers and the aquatic contaminants.

Field samples
Fish were collected by otter trawl fishing. Guanabara Bay (GB) was sampled every 15 days from June 2015 to December 2016. A total of 84 burrfishes (Chilomycterus spinosus) and 98 corvines (Micropogonias furnieri) were sampled from GB. The reference site, IB, was sampled every 15 days during 2016. The total number of samples from IB were 55 for the burrfish and 60 for corvine. Samples were subject to standard procedure of measurement, weighting, and sexing after sacrificed by spinal severance. Calculation of the condition factor (CF) was done following the description of Jisr et al. (2018), as Subsequently, tissues were dissected out, weighed, and stored at −20 °C until analysis. The gallbladder was also dissected, and bile content was extracted and stored at −20 °C to maintain sample stability until analysis. An abdominal incision was performed for macroscopic sex identification.

AChE determination
Muscle tissue was homogenized in a buffer (potassium phosphate 0.1 M, pH 7.2). The resultant solution was centrifuged, and the supernatants were collected and used as an enzyme extract to determine AChE activity. Such activity was measured in triplicates following Oliveira-Silva et al. (2000). Analyses were perform by incubating 50 ml of each sample together with 4 ml of buffer (sodium phosphate 120 CF = body weight(g) × (length(cm) 3 −1 × 100 mM ± pH 7.6) and 1 ml of DTNB 2 mM at 25 °C for 1 min. Acetylthiocholine iodide was added to initiate the reaction (1 ml, 6.6 mM in distilled and/or deionized water) in the incubated mixtures. Immediately after addition, absorbance was measured at 412 nm; the obtained results were used as blank (T0). Further measurements were done after 1 and 2 min of adding the acetylthiocholine solution. In order to calculate enzymatic activity, the average absorbance variation per minute was used. AChE-specific activity was determined using standard curves of L-cysteine and bovine albumin following the formula: where P is concentration of protein (mg/ml); E, activity (mmoles/min/ml); and Ea, specific activity (mmoles/ min/mg of protein).
The Lowry method (Waterborg, 2009) was used in triplicates to determine the protein content of each sample. An albumin solution was used as standard and a wavelength of 660 nm.

MT determination
Each liver tissue was weighed and homogenized at 4 °C in four volumes of 0.02 M Tris-HCl (pH 8.6) buffer in an ice bath. An aliquot of each homogenate (3 mL) was centrifuged at 30,000 g for 20 min at 4 °C. The supernatant was separated from the pellet, diluted 1:10 in 0.9% of NaCl solution, heated 70 °C for 10 min, and consequently centrifuged under the conditions described above. A 10 µL of supernatant was used for the MT determination.
Differential pulse polarography analyzed MTs based on -SH group quantification according to Erk et al. (2002). The analyses were made using 10 mL of electrolytic solution of ammonium buffer (hydroxide + ammonium chloride 1 M, pH 9,5) contend 0.6 mM of cobalt hexamine. The electrolytic solution was degasified with nitrogen gas for 10 s. MT was measured during a potential scan between −0.9 and −1.7 V (Autolab

PAH metabolites in fish bile
Bile content was first thawed and sonicated for 15 min, and then diluted 1000 times in 1:1 ratio of water:ethanol solution for measurement in the Varian Cary Eclipse Spectrofluorimeter in order to extract the PAHs. Sigma Aldrich ® standards were diluted in ethanol to prepare standard PAHs solutions. PAH metabolites of 2, 4, and 5 rings of pyrogenic and pyrolytic origin were assessed using the wavelength fluorescence method described by Rodríguez and Sanz (2000). Additionally, the 1-hydroxypyrene was likewise analyzed. Metabolites of 1-hydroxypyrene, naphthalene, pyrene, and benzo(a) pyrene were analyzed in excitation (λexc) and emission (λem) wavelength pairs of 370/350 nm, 270/335 nm, 340/390 nm, and 380/430 nm, respectively.
Statistical analysis Statistical differences were tested using analysis of variance (ANOVA) or by Student's t tests. Post hoc Tukey tests were applied to define statistical differences among means (Statsoft, 2011). A level of probability below P = 0.05 was estimated statistically significant. Values are presented as the means ± standard error. Statistical analyses were carried out by SPSS software version 21.0.

Biological parameters
Biological measurements of both fish species are found in Table 1. Animals from the reference site, IB, had higher size and weight values than those from GB in both fish species. This difference was remarkable in corvine (Micropogonias furnieri), where means of weight and size at IB were 277.50 g and 30.45 cm respectively, while those values were 29.42 g and 14.21 cm at GB (P < 0.05). Similar pattern was observed in burrfish Since the length of fish species relates to sexual maturation (Vazzoler, 1991), individuals of corvine were divided in two categories, sexually mature and sexually immature. This analysis was carried out to verify if the difference in size between both sampling sites in corvine would interfere with the values of the biomarkers under study. Significant difference between sexual maturation and size among sites was not found (P < 0.05), which indicated that sexual maturation did not interfere with the biological factors analyzed.
An analogous analysis was carried out to evaluate if the sex of the corvine (male and female samples) would affect the values of the biomarkers under study, and similar results were obtained (P < 0.05). Therefore, after ruling out the possibility of size and sex interfering with our study, all the samples were grouped and analyzed as one. All individuals were grouped to verify the potentiality of CF, AChE, MT, and PAH in these fish species as biomarkers to determine the impact of pollution in this water body.

CF values
Significant differences in condition factor (CF) values were detected among sites ( Fig. 2A) (P < 0.05) in both fish species. Fish from GB had significant lower values of CF than those from the reference site, IB. The mean of CF values of burrfish collected at IB was (5.92), whereas that of the burrfish collected at GB was 5.14. Corvina showed an average of CF values of 1.08 at IB and 0.92 at GB.

AChE activity
Muscle AChE activity showed significantly lower values (P < 0.05) at GB in both fish species (Fig. 2B) than at the reference site, IB. The mean of AChE activities in burrfish collected at GB was 1.169 μmol.min −1 .mg ptn 1 , while the value of those collected at IB was 1.49 μmol.min −1 .mg ptn 1 . In the same fashion, activity of AChE in corvine were higher at IB (1.25 μmol.min −1 . mg ptn 1 ) than at GB (0.84 μmol.min −1 .mg ptn 1 ).

MT levels
Levels of MT were significantly higher in both fish species at GB than at the reference site, IB (Fig. 2C) (P < 0.05). MT levels of burrfish were 2.34 mg/g at GB and 1.15 mg/g at IB. Corvine presented levels of MT of 1.07 mg/g at GB and of 0.40 mg/g at IB.

PAH metabolites levels
Average concentrations of PAH metabolites, such as naphthalene, pyrene, benzo(a)pyrene, and 1-hydroxypyrene, were more elevated in GB compared to IB in both fish species. For corvine, metabolites of pyrene, benzo(a)pyrene, and 1-hydroxypyrene showed a substantial difference between samples from both sites, GB and IB (P < 0.05), for all three metabolites (Fig. 3A). In the case of naphthalene metabolites, significant differences among sites were only found on burrfish samples (Fig. 3B). The bile samples from burrfish show statistically relevant differences between GB and IB only for the naphthalene metabolites (Fig. 3B).

Discussion
GB receives untreated domestic effluents produced by approximately 11 million inhabitants (> 470 tons of organic matter); 64 tons of industrial wastes containing organic matter, nutrients, hydrocarbons, heavy metals (estimated in 0.3 ton/day) and suspended solids; 7 tons of oil; and 6 tons of garbage, daily (Baptista Filho et al., 2019). The texture of the sediment (particle size and organic carbon content), hydrodynamic, and bathymetry play an important role in the concentration Fig. 2 Concentration of CF, AChE and MT activity found for both fish species at GB and IB. A CF values of Micropogonias furnieri (corvine) and Chilomycterus spinosus (burrfish) at the two locations. Values are means ± SE. Asterisk (*) indicates significant difference between locations (P < 0.05). B AChE activity in muscle of Micropogonias furnieri and Chilomycterus spinosus at the two locations. Values are means ± SE. Data are expressed as μmol/min/g protein. Asterisk (*) indicates significant difference between locations (P < 0.05). C Levels of hepatic MT in Micropogonias furnieri and Chilomycterus spinosus at the two locations. Values are means ± SE. Data are expressed as μg/g liver. Asterisk (*) indicates significant difference between locations (P < 0.05)

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Vol.: (0123456789) A B C of contaminants in GB and vary depending on the concentration present at the rivers and channels that discharge into the bay cross greatly urbanized areas, receiving all kinds of effluents (Faria & Sanchez, 2001, Fonseca et al., 2013, Borges et al., 2014. The average indicators value for GB are salinity, from 13 to 36 mg/L; dissolved inorganic nitrogen 0.4 to 4.4 mg/L, total phosphorus 0.1 to 6.03 mg/L; dissolved oxygen 6.2 to 8.4 mg/L; biological oxygen demand 4.1 to 11.3 mg/L; and fecal coliforms 685 to 250,000 (Fries et al., 2019). In such environment, the concentration of metals in soluble phase is very low in comparison with the concentration present in the sediments, affecting their bioavailability. In fact, studies using selective sequential extraction procedures have shown that most of the metals in the sediments are associated to organic matter and there is a high probability that some of them, especially Zn, Cu, Cr, Ni, and Pb, contaminate the water column in case of any disturbing intervention (Cordeiro et al., 2015;Covelli et al., 2012;Fonseca et al., 2013).
A number of studies have reported on the bay's condition (Fries et al., 2019;Silveira et al., 2017;Neto et al., 2017;Abreu et al., 2016;de Assis Costa et al., 2018;Fistarol et al., 2015;Soares-Gomes et al., 2016). However, the real impact that such pollution has on the environmental quality of the estuary and how it affects the biota remains unclear.
Until date, few studies have informed the effects pollution on the GB has on its biota by using biomarker responses, and they have done it analyzing responses to a type of pollutants. Such as the employability of a set of biomarkers on catfish that was efficient for screening the effects of pollution for PAHs, reporting the GB with worst water quality than Sepetiba and Ilha Grande Bays (Freire et al., 2021). Another study Fig. 3 Average concentration of PAH metabolites found in corvine and burrfish from GB and IB. A Average concentration of pyrene, benzo(a)pyrene, and 1-hydroxypyrene metabolites. B Average concentration of naphthalene metabolites. Asterisk (*) denotes significance difference between locations (Test T-student; P ≤ 0.05) A B reported that AChE activity was decreased in two fish species sampled from GB (de Castro Rodrigues et al., 2018) indicating effects of pesticide in the biota. Concerning metals, MT variability was linked to metal environmental exposure in some fish species in GB (Hauser-Davis et al., 2021). However, there is no study that would assess the effects of the complex mixture of pollutants in the biota. Our study compiles information on how the current status of the bay is affecting two fish species in the area by using a multibiomarker approach. Both fish species, Micropogonias furniture (corvine) and Chilomycterus spinosus (burrfish), have shown to be an adequate species when assessing the effect of aquatic pollution in ecosystems. The results of the present research show the effectiveness of integrating a group of biomarkers to outline the exposure and the effects of human activities.
By way of a primary screening biomarker, the condition of the total body was utilized, as determined with the CF values. Our results revealed that CF values were lower in fishes from GB indicating that these fishes were under higher environmental stress. Similar findings were reported for fish living in highly polluted environments (Kasimoglu, 2014;Linde-Arias et al., 2008). Nevertheless, CF is a gross index that shows the overall effect of contamination on fish (Van der Oost et al., 2003), which may not provide evidence of specific responses to the toxic elements in the environment. Additionally, CF values could also be influenced by other causes, such as food source and availability, and season of capture (Webb et al., 2005).
More specific biological responses would allow discriminating and distinguishing the effects of different types of contaminants at the bay. Hence, AChE activity inhibition was selected as a biomarker of the effects of organophosphorus and carbamates, which are pesticides broadly utilized in food production (Assis et al., 2012;Eto, 2018). Discharges from areas with heavy use of pesticides are discharged into the GB's water through around 45 tributaries. Our results show that AChE activities were significantly lower in both fish species at GB. This not only points out the presence of cholinesterase inhibitors in GB but also their availability and effects on the biota. Our results show the significance of monitoring the presence of pesticides within the bay waters, due to neurological damage that exposure and consumption of these molecules may cause on biota and humans (Kapeleka et al., 2019). Therefore, the use of AChE activity as biomarker in these two fish species has demonstrated to be a valuable instrument to analyze the effects of contamination allowing to demonstrate neurotoxic effects.
MT activity was utilized to evaluate the effects of metals in the GB estuary, which is seriously affected by metal contamination. (Fistarol et al., 2015). Studies have indicated that despite such great input of metals, their biological availability was limited as a result of the high organic carbon and sulfide content (Kehrig et al., 2003;Machado et al., 2002) conferring low potential of remobilization and biotic uptake. Our results show that despite pollutants being interred in anoxic sediments and thus with presumably low biological uptake, metal pollution is indeed affecting the biota of GB, causing significant higher levels of MT proteins. Furthermore, they indicate that metals are available to the biota in GB in spite of the elevated carbon and sulfide content. Furthermore, a closer monitoring of the metal pollution may be needed in the bay, as the availability of metals may present a threat not only to the biota but also to human consumption. In this study, the use of MT as a biomarker enhances the understanding of how metal pollution affects the biota in a reducing environment.
Bile metabolites have been widely used to determine PAH exposure in fish (Linde-Arias et al., 2008;Moreira Freire et al., 2020). Environmental monitoring using these metabolites includes the most frequent and prioritized PAHs associated with environmental contamination (Moreira Freire et al., 2020). These molecules cover PAH metabolites from different origin, petrogenic origin, such as unburnt fuels, and petroleum compounds of pyrolytic origin (burnt fuels).Our results demonstrate that both fishes presented higher concentrations of PAH metabolites in GB than in the reference site. However, burrfish shows statistically relevant differences between sites only for pyrolytic metabolites whereas was not very sensitive to differences regarding PAH metabolites of pyrogenic origin. The distinct responsiveness that burrfish and corvine have to the effects of PAH contamination may be attributed to several factors, including metabolic, dietary and regional differences (Logan, 2007). Such results indicate the existence of physiological differences in the responsiveness of different species to environmental contamination (Van der Oost et al., 2003). For this reason, the use of more than one bioindicator species in the assessment of contamination of aquatic environments is important, since the two species studied responded differently to exposure to PAHs.
This study indicates and strengthens the necessity for effective action to monitor and conserve the bay, such that ensures appropriate water quality and health for both humans and the environment. This could point out the need to implement technologies of treatment and/or remediation of industrial and domestic effluents to reduce the exposure.

Conclusion
For all the analyzed biomarkers, our results indicate that fish from GB are affected by pollutants. The levels of MT in both species in GB reflect the consequences of heavy metal exposure in the bay, in spite of their biological availability being restricted by the high organic carbon and sulfide content of the tropical estuary. The AChE activity indicates that both species are vulnerable to the presence of carbamates pesticides in the bay waters. The levels of PAHs were higher in GB for both fish species, indicating exposure to these substances. However, the fish species showed different behavior regarding the origin of the PAHs. This highlights the importance of using more than one sentinel species in the environmental assessment of complex pollution. To conclude, the multibiomarker approach used in this study evidently depicted effects on the health of fish in a waterbody with a complex polluted situation and further categorized the effects of anthropogenic activities in this aquatic system.