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Article

Investigating the Epigenetic Effects of Polystyrene Nanoplastic Exposure in Bluegill (Lepomis macrochirus) Epithelial Cells Using Methylation-Sensitive AFLPs

by
Sheridan M. Wilkinson
1,2,
Justine M. Whitaker
3 and
Alexis M. Janosik
1,*
1
Department of Biology, University of West Florida, Pensacola, FL 32514, USA
2
Department of Physiological Sciences, University of Florida, Gainesville, FL 32608, USA
3
Department of Natural Science, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
*
Author to whom correspondence should be addressed.
Microplastics 2025, 4(1), 10; https://doi.org/10.3390/microplastics4010010
Submission received: 31 October 2024 / Revised: 19 January 2025 / Accepted: 24 February 2025 / Published: 27 February 2025
(This article belongs to the Topic Microplastics Pollution)
Browse Figures
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Abstract

:
Microplastics, remnants of macroplastics that have broken down to fragments smaller than 5 mm, and nanoplastics, broken down even further to sizes < 1 μm, are pervasive in aquatic ecosystems. These plastic particles are consumed by microscopic organisms, leading to bioaccumulation up trophic levels. The accumulation of plastic in the organismal gut can result in various repercussions, including cellular contamination and genomic modifications such as DNA methylation. While methylation has been studied in teleost fishes, the impact of nanoplastic exposure on this process in any species remains largely unexplored. This study delves into this largely uncharted territory, investigating the accumulation of methylation due to nanoplastic exposure within the genome of cultured bluegill BF-2 cells (Lepomis macrochirus) using methylation-sensitive AFLPs. The methylation state was analyzed through capillary gel analysis and electropherograms. Differential methylation occurred between several control and experimental groups due to nanoplastic exposure; however, these differences were not dose- or time-dependent. These results could suggest that higher dosages and exposure times to nanoplastics do not result in increased methylation levels in congruence with the dosage and exposure time; rather, only the presence of nanoplastics is enough to cause DNA methylation changes.

1. Introduction

1.1. Microplastics

Since the first reports of marine plastic debris in the early 1970s [1], the demand for plastic has increased dramatically. Plastics are made from several different polymers, including polyethylene, polypropylene, polystyrene, polyethylene terephthalate, and polyvinyl chloride [2]. These polymers create a highly durable, lightweight, and cost-efficient material with many applications, which has led to an explosion in the manufacturing and usage of plastics in the decades since its invention. Due to its high durability and ubiquitous uses, plastics are also a pervasive pollutant with a long residence time in the environment. For instance, Chamas et al. (2020) reported that high-density polyethylene degraded at 0 to 11 μm/year, translating to an estimated half-life of 58 years for bottles and 1200 years for pipes [3].
Much research has been conducted studying the pervasiveness of plastics in marine ecosystems, with surveys indicating that 60–90% of marine debris is composed of plastics; however, many of these surveys focus primarily on macroplastics [4,5,6,7,8]. Microplastics, however, are much more difficult to quantify. Unfortunately, research into the abundance of microplastics in freshwater ecosystems has received less attention than in marine ecosystems. Data on surface water abundance are deficient in larger water bodies and practically non-existent in smaller waterways [9]. However, several recent studies have shown the occurrence of microplastics in surface waters [10], sediment, and biota [11] of even rural freshwater systems. In 2020, Gopinath et al. reported on the presence of microplastics in both the sediment and surface water of the Red Hills Lake in Chennai City, India [12]. Microplastics were also reported in the surface water of the River Thames, United Kingdom [13]. Additional reports of microplastic pollution have also come from Lake Victoria, East Africa [14]; Surabaya River, Indonesia [15]; Haihe River, China [16]; Ofanto River, Italy [17]; and several other freshwater systems around the world [18]. This is unsurprising as freshwater ecosystems are likely subject to the same pollution mechanisms as their marine counterparts. Freshwater species are also expected to suffer the same or similar impacts of microplastic consumption as marine species.
Microscopic aquatic organisms, such as zooplankton, have been documented to consume microplastics, which bioaccumulate up trophic levels [19,20]. A study by Mattsson et al. (2016) tracked the transfer of plastic particles from dosed algae (Scenedesmus sp.) to zooplankton (Daphnia magna) to crucian carp (Carassius carassius) [20]. While plastic fragments can be excreted or egested out of an organism’s system, bioaccumulation occurs when the egestion mass is less than ingestion [19]. However, ingestion is not the only way organisms can uptake plastic fragments. Plastic fragments can also be absorbed through the gills, as was demonstrated by Watts et al. (2014) when crabs were exposed to 8–10 µm polystyrene microplastics [21]. Absorption of microplastics through the gills had a noticeably longer residence time within the body of crabs treated than those that ingested them [21]. This is likely because there is no distinct route of excretion when plastic fragments are taken in through the gills.
The accumulation of plastics in organisms can have severe consequences, including reduced food uptake and starvation due to blockage in the digestive tract. Chemicals used in producing plastics can also leach into the cells of organisms, and plastic fragments can be transposed from the gut to adjacent tissues. Furthermore, microplastic fragments can act as vectors for other contaminants, such as hydrophobic pollutants, significantly amplifying the ecological impact. For instance, Tanaka et al. (2013) found polybrominated diphenyl ethers in the abdominal adipose tissue of short-tailed shearwaters [22]. This compound was only detected in plastic particles found in the stomachs of birds that tested positive for the said chemical. Microplastic fragments can also hinder physical movement through ingestion [23].
Microplastics can also be further broken down both in the environment and inside the organism into nano-sized particles (10−9–10−7 m) [24,25,26,27]. These nano-sized particles are believed to pose an even more significant threat than microplastics, as they can pass through biological barriers [20], penetrating tissues [28], accumulating in organs [29], and affecting the behavior and metabolism of organisms [20,30,31]. This highlights the urgent need for further research and action to address this escalating issue.
Microplastic exposure has also been found to affect gene expression in model organisms (Danio rerio) [32,33]. For instance, Karami et al. (2017) investigated how exposure to pristine low-density polyethylene fragments at 5, 50, and 500 μg/L for 10 or 20 days altered the expression of several biomarkers in zebrafish larvae [32]. While significant changes were not observed in the selected biomarkers on days 10 or 20, transcription of several critical biomarkers was markedly lower in the larvae on day 20 compared to day 10. LeMoine et al. (2018) examined how exposure for 14 days to 5 and 20 mg/L concentrations of fluorescently labeled polyethylene particles affected the development, growth, and metabolic rates of embryonic and larval zebrafish, as well as effects on the transcriptome of the fish [33]. Again, transcriptomics via RNA sequencing revealed that widespread, yet temporary, changes in gene expression occurred within 48 h of exposure during a critical development period [33].
Differences in expression may also be linked to epigenetics, but little work has been carried out to examine how microplastic exposure affects epigenomes via global methylation changes. Exposure to bisphenol A—an essential component in the production of plastics—has shown differentially methylated genes essential in axon targeting, synaptic development, and neuronal survival in zebrafish embryos via whole-genome bisulfite sequencing [34]. Given that most aquatic organisms intentionally or accidentally ingest micro-/nanoplastics [21,35,36,37,38,39,40], it is entirely possible that exposure could alter the global methylation state of the affected organisms. Alteration of global methylation can be inferred from the studies conducted by LeMoine et al. (2018) and Olsvik et al. (2019) [33,34]; however, it has not been directly studied how whole microplastics affect the global methylation in the fish genome.

1.2. Epigenetics and DNA Methylation

Epigenetics studies genomic modification in organisms due to environmental conditions [41]. These genome modifications are not changes to the DNA sequence itself but, instead, affect how genes are regulated by the cell [42] and can, in turn, cause variation in morphology [43,44,45]. Examples of epigenetic changes include chromatin remodeling, deacetylation and modification of histones, position effects, and interference by small RNAs [46]. However, the epigenetic mechanism that has been studied the most is DNA methylation. Methylation is most commonly seen when a methyl group is attached to a cytosine followed by a guanine, known as CpG sites [47], and is often associated with decreased gene activity [47,48].
Methylation is naturally seen in the genome through the removal and addition of methyl groups via demethylases and methyltransferases, which alter the structure and function of the chromatin and, therefore, promote and silence gene transcription, respectively [49]. Specific methylation patterns can naturally be seen in response to development and cellular differentiation [50,51] and sex determination [52]. External factors, like temperature [53,54] and environmental contaminants [55,56], can also alter the methylation state of impacted organisms. As such, unnatural methylation could potentially repress the transcription of essential genes, hindering important cell functions, such as hormone production [54]. Changes in patterns of whole-genome DNA methylation can also be significant in development and cellular differentiation, as has been well documented in mammals [57]. These changes have also been studied in fungi, plants, and invertebrate animals [58,59,60].
All organisms naturally accumulate methylation in their genomes throughout their lives, and a certain degree of methylation is necessary for natural processes, such as development and growth [50,51]. For instance, DNA methylation is related to cell morphology and metabolism through gene expression regulation and epigenetic stability, and deviant DNA methylation can modify cytoskeletal organization genes (i.e., genes like RhoA and Rac1, critical regulators of actin cytoskeleton remodeling), affecting cell shape, adhesion, and migration [61,62,63]. However, the presence of pollutants, such as plastics, in the environment could exacerbate the methylation process and affect cell function. An increasing number of studies have begun to look at the effects of environmental pollutants, like microplastics or chemicals, and how these toxicants change the methylation patterns in exposed organisms, especially in marine fishes.
Nanoplastics have been shown to induce DNA methylation changes through several mechanisms at the molecular level. One key mechanism is oxidative stress, triggered by increased reactive oxygen species production due to nanoplastic exposure [64,65]. This oxidative stress can activate epigenetic regulatory mechanisms, such as DNA methyltransferases, which modify cytosine residues in CpG islands and potentially silence gene expression. For example, altered methylation patterns can affect stress-related and DNA repair genes and apoptotic pathways due to reactive oxygen species activity and associated damage responses [64]. Direct interaction of nanoplastics with cellular machinery is another mechanism that can induce methylation alterations, as nanoplastics have also been shown to disrupt mitochondrial function and impair autophagy, further escalating cellular stress responses. This cascade can lead to abnormal activation or repression of methylation-related pathways, affecting genes involved in inflammation, metabolism, and other critical processes [65]. Transgenerational effects are also possible, as studies have shown that nanoplastics can induce changes in germ cells, suggesting that epigenetic alterations may be heritable, which could be concerning for long-term health and development [66].

1.3. Bluegill Sunfish

Bluegill sunfish (Lepomis macrochirus), the focus species of this study, is a common fish native to the freshwater lakes, rivers and streams, ponds, reservoirs, and swamps of North America, ranging from Canada to northern Mexico [67]. Lepomis macrochirus has also been introduced to many countries worldwide, including Brazil, Iran, Japan, Korea, Madagascar, the Philippines, and Venezuela [67]. Generally, L. macrochirus inhabits silty, sandy, or gravel substrates near rooted aquatic plants in shallow waters up to a depth of 5 m [67].
Because L. macrochirus is widespread and prolific, it has been the focus of many ecotoxicological studies [68,69,70,71]. The use of L. macrochirus as a model organism is likely attributable to the ability to perform cell culture on BF-2 caudal trunk cell lines derived from bluegills, which have been suggested to be sensitive indicators of aquatic pollutants [71], negating the need for aquaculture of the whole animal.
As such, this study aimed to investigate how prolonged and increasing amounts of microplastic exposure can affect the genome of L. macrochirus BF-2 cell lines by looking at differential methylation of CpG sites using methylation-sensitive AFLPs (MS-AFLPs). Downregulation of proteins due to methylation could have consequences on reproduction, metabolism, growth, and development, as well as other biological processes of bluegills and other aquatic organisms. The results of this study are beneficial for this field of research, as knowing the global ramifications of plastic pollution on the epigenome will help to inform more targeted studies in the future.

2. Materials and Methods

2.1. Culture of BF-2 Cells

Bluegill BF-2 cells (ATCC CCL-91) [72,73] were acquired from C. Lavelle (Environmental Protection Agency) and were stored in liquid nitrogen. When ready to begin culturing, the frozen BF-2 cells were resuscitated using standard methods [74]. The complete media used comprised 500 mL HyClone™ Minimum Essential Medium (MEM) (Cytiva®; Marlborough, MA, USA), 10% fetal bovine serum (FBS; 50 mL), and 1× Gibco™ Antibiotic-Antimycotic (100×; 5.5 mL; ThermoScientific®; Waltham, MA, USA). Upon completion of the resuscitation protocol, the cells were transferred to a sterile T-25 flask and incubated at 37 °C, 5% CO2, until the flask was at least 90% confluent.
To obtain adequate cells for three 6-well plates, the cells were split and plated onto three T-75 flasks. All subsequent subculturing of the BF-2 cells was carried out using standard methods [74]. When the cultures in the T-75 flasks reached a 95% confluency, cells were split evenly into each well of a 6-well plate, with the top left well serving as the control. The remaining five wells were then dosed with microplastics according to the experiment’s parameters. Three plates were created for each experiment for three sampling time points (T1 = 24 h, T2 = 48 h, and T3 = 72 h). Following the dosing period for each plate, the cells were gathered, and 2 mL of cells were transferred to labeled 2 mL Eppendorf tubes and pelleted at 9000 rpm for 3 min, and the supernatant was aspirated off. The media of each plate was also replaced every 24 h until the cells were ready to be sampled.

2.2. Nanoplastic Treatment

The experimental cultures were dosed with 0.04–0.06 μm Spherotech® (Lake Forest, IL, USA) nile red fluorescently dyed polystyrene nanoplastic particles with a base concentration of 2.84 × 1011 particles at time 0 for each experiment, which is a dosage much higher than environmentally relevant concentrations. However, bioaccumulation of nanoplastics within cells and tissues of wild populations is not well quantified, so nanoplastic concentrations within organisms could be much higher than in the surrounding environment. As such, this was the smallest concentration that could be reliably quantified.
Specifically, four different experiments were employed. Experimental cultures in experiment I were continually dosed at the base concentration for T1, T2, and T3 (Table 1). Control cultures for each experiment were not dosed with microplastic particles. In experiment II, all experimental cultures were given an initial dose of 5.68 × 1011 particles, and plates 1, 2, and 3 were sampled at T1, T2, and T3, respectively (Table 1). For experiment III, all experimental cultures were given an initial dose of 8.52 × 1011 particles, and plates 1, 2, and 3 were sampled at their respective time points, T1, T2, and T3 (Table 1). Lastly, for experiment IV, all the experimental cultures were dosed with the base concentration, and plate 1 was sampled at T1; at this time, plates 2 and 3 were dosed with 5.68 × 1011 particles. At T2, plate 2 was sampled, and plate 3 was dosed with a final concentration of 8.52 × 1011 particles and then sampled at T3 (Table 1). This strategy for experiment IV was used to mimic bioaccumulation.

2.3. MS-AFLP Analysis of the Methylation State

The following protocol was derived from protocols published by Vos et al. (1995) [75], who described one of the original AFLP protocols, and Xiong et al. (1999) [76], with modifications from personal communication (J.M. Whitaker).
DNA was extracted from the preserved culture samples using the DNeasy Blood and Tissue Kit (Qiagen®; Hilden, Germany). Extracted DNA samples were then run through the following MS-AFLP protocol. DNA concentration was determined using a Nanodrop 2000 Spectrophotometer (ThermoScientific®). The final DNA concentration was 40–60 ng/µL.
The digestion reaction master mix added to 4 µL of sample DNA consisted of 4.5 U EcoRI enzyme (0.45 µL), 3 U HpaII/MspI enzyme (0.3 µL), 3.5 µL 10× NEB buffer, 0.2 µL 0.1% BSA, and 11.55 µL ddH2O to reach 20 µL [77]. See Figure S1 in Supplementary Materials for information on when the HpaII and MspI enzymes will cut CpG sites. The reaction was incubated for 4 h at 37 °C and then inactivated at 65 °C for 10 min. The restriction enzymes and buffer were purchased from New England Biolabs® (Ipswich, MA, USA).
Following digestion, double-stranded adaptors were ligated to the ends of the restriction fragments. Ligation was performed at 16 °C for 17 h using 10 µL digested DNA, 350 U T4 DNA ligase (0.875 µL), 2.0 µL 10× T4 DNA ligase buffer, 2.0 µL 5 µM EcoRI adaptors (Table S1 in Supplementary Materials), 5.0 µL 50 µM HpaII/MspI adaptors (Table S1 in Supplementary Materials), and 0.125 µL ddH2O to reach 20 µL [77]. The DNA ligase and buffer were purchased from New England Biolabs® (Ipswich, MA, USA).
Pre-selective PCR of the ligated DNA product was completed with the following reaction master mix: 2.0 µL DreamTaq (ThermoScientific®) PCR buffer, 0.5 µL 10 mM dNTPs, 2.0 µL 10 µM EcoRI primer (Table S1 in Supplementary Materials), 2.0 µL 10 µM HpaII/MspI primer (Table S1 in Supplementary Materials), 0.6 µL 5 U/µL DreamTaq polymerase, and 11.9 µL ddH2O. This master mix results in 19.0 µL per reaction that was aliquoted and added to 1.0 µL of ligated DNA for a total reaction volume of 20 µL. The reactions were run on the following thermocycler protocol with an initial denaturing step of 94 °C for 5 min, followed by 94 °C for 30 s, 56 °C for 1 min, and 72 °C for 1.5 min for 25 cycles. A final extension was performed at 72 °C for 10 min, then 60 °C for 30 min, and held at 4 °C indefinitely [68]. Once the PCR was completed, 10 µL of PCR product from each reaction was diluted in 90 µL of milli Q water and run on a 1–2% agarose gel to ensure amplification occurred.
The selective PCR master mix was created as follows: 2 µL DreamTaq PCR buffer, 0.5 µL 10 mM dNTPs, 1.5 µL 10 µM FAM labeled EcoRI primer (Table S1 in Supplementary Materials), 1.5 µL 10 µM HEX labeled EcoRI primer (Table S1 in Supplementary Materials), 1 µL 5 µM CAT/CAC primer (Table S1 in Supplementary Materials), 0.6 µL DreamTaq polymerase, and 7.9 µL ddH2O. Two different primer combinations, master mix A (MMA) and master mix C (MMC), were used on all samples for selective PCR; the reagents and volumes used for MMA and MMC are summarized in Table S2 in Supplementary Materials. The 15 µL of the master mix was then aliquoted and added to 5.0 µL of the dilute pre-selective PCR product for a total reaction volume of 20 µL. The reaction mixtures were then run on the standard thermocycler protocol: (1) 94 °C for 5 min, (2) 94 °C for 30 s, (3) 64 °C for 30 s decreasing by 0.7 °C per cycle, (4) 72 °C for 1.5 min, (5) go to step 2 13×, (6) 94 °C for 30 s, (7) 56 °C for 30 s, (8) 72 °C for 2 min, (9) go to step 6 20×, (10) 72 °C for 10 min, (11) 60 °C for 30 min, and 12) 4 °C indefinitely [77]. Selective PCR products were then run through capillary electrophoresis at Yale’s DNA Analysis Facility on Science Hill.

2.4. Fragment and Statistical Analysis

Fragment analysis was performed by S.M. Wilkinson using Geneious Prime 2020.1.2 (Auckland, New Zealand) and the Microsatellite plugin [78]. Fragments less than 70 bp and greater than 500 bp were excluded from the analysis, as they fell outside the size standard used. Fragments were scored in a binary fashion with uninformative states in which a peak absent from both reactions was scored as missing [79]. Using this binary data, the results of the restriction fragments were run through the msap package [80] in R [81].
For the determined methylation susceptible loci (MSL) in each grouping, the frequencies of unmethylated, hemimethylated, internal cytosine methylated, and fully methylated/target absent loci were ascertained. These frequencies were revealed by grouping the experimental cultures to determine the average methylation and comparing the average to the control culture for the respective time group. Experimental cultures were grouped by their time groups (T1, T2, and T3) for each experiment. Statistically significant differences between the frequencies of methylated loci between the controls and experimental time groups, as well as between the time groups (T1 × T2, T1 × T3, and T2 × T3), were determined via the χ2 test (chisq.test()) function [82] in R [83]. Statistical significance was determined using a p-value < 0.05.

3. Results

The MS-AFLP protocol used in this study provided polymorphisms for data analysis. Specifically, two primer combinations (MMA and MMC) allowed many polymorphisms to be detected; 51–130 peaks out of the 232–306 scored peaks (21.5–46.4%) were polymorphic. The frequencies of MSL and non-methylated loci (NML) for each time group in each experiment as determined by msap are summarized in Table 2 for MMA and Table 3 for MMC. For MMA, most loci (64.1–78.6%) were shown to be methylation-susceptible, while only 21.4–35.9% of loci were not methylation-susceptible. Most of the polymorphisms also fell into the category of MSL, with 64–116 (33.2–52.7%) polymorphic loci, while few of the NML were polymorphic, 0–14 (0–23.3%). For MMC, most loci (59.5–70.6%) were also methylation-susceptible, and non-methylated loci comprised only 29.4–40.5% of the loci found. MMC also displayed many polymorphic MSL (32.2–43.2%) and a comparatively low frequency of NML (0–10%).

3.1. MMA

The percentage of no methylation, hemimethylation, internal cytosine methylation, and full methylation for each time group and experiment for MMA can be seen in Table 4 and Figure 1, along with the p-values from the χ2 analysis between each time group and its respective control and between temporal groups in Table 5. There did not seem to be a pattern of a specific time group showing statistically significant differential methylation, as the only statistically significant time groups were T3 in experiment I, T2 and T3 in experiment III, and T1 in experiment IV. Interestingly, methylation patterns between the different time groups showed less variation with each other than each group displayed with its control, which might suggest exposure time is not a significant factor for any increased methylation, and only the presence of microplastics is enough to cause differential methylation (Table 4 and Table 5).

3.2. MMC

As with MMA, the percentage of no methylation, hemimethylation, internal cytosine methylation, and full methylation for each time group and experiment for MMC can be seen in Table 6 and Figure 2, and the p-values from the χ2 analysis between each time group and its respective control and between temporal groups in Table 7. Also, as with the experiments run through MMA, there was no clear pattern of statistically significant differential methylation, and T1 × T2, T1 × T3, and T2 × T3 tended to show less variation with each other than the time groups showed with their controls (Table 6 and Table 7). The only statistically significant time groups were T3 in experiment I, T1 and T2 in experiment II, T2 and T3 in experiment III, and T3 in experiment IV.

4. Discussion

In this study, MS-AFLPs revealed that the majority of loci in bluegill BF-2 cell lines across both master mixes and all four experiments were methylation-susceptible and, therefore, vulnerable to epigenetic change as a result of nanoplastic exposure. MSL also displayed far more diversity in the form of more polymorphisms than their non-methylated counterparts. As for the estimated methylation patterns among the MSL, these results were unexpected. It did not appear that any one concentration or exposure time equated to significantly more methylation within the sampled genomes, but rather, the mere presence of nanoplastics could alter the methylation state. Perhaps the most interesting results were that the different time groups for each of the four experiments showed less differentiation with each other than they did with the controls. This suggests that exposure time was not a significant factor for increased methylation in the experimental cultures and that only the presence of nanoplastics is enough to cause methylation.
Full methylation of the experimental cultures never exceeded 56.85%, but generally, the frequency of full methylation fell well below this number. Perhaps this presents a threshold for the maximum amount of methylation for continued cell functionality in bluegills. It would be interesting to determine the maximum methylation allowed before cell death occurs, as this could be useful knowledge for future ecotoxicology studies and determining the bare minimum genes that must be expressed for continued cell functionality. However, a study conducted by Morán et al. (2013) found that full methylation in brown trout (Salmo trutta) gill tissue was noticeably higher (57.5–63.6%) than the numbers reported in the current study, with the control group having a full methylation frequency of 61.8% [83]. However, gonad, kidney, and gill tissue in half-smooth tongue sole (Cynoglossus semilaevis) displayed full methylation frequencies of 15.97 ± 1.22%, 17.5 ± 4.11%, and 16.68 ± 5.34%, respectively, in females and 17.47 ± 7.04%, 18.64 ± 5.60%, and 12.06 ± 7.21%, respectively, in males [77]. As brown trout and bluegill are both freshwater species, perhaps this suggests that freshwater fish have higher frequencies of full methylation than their marine counterparts, like the half-smooth tongue sole. However, differences in methylation patterns between marine and freshwater fish warrant further investigation.
This is an emerging field of study, as most studies in a similar vein have not looked at the global methylation response to plastic exposure, and very little research involving micro- or nanoplastics is available to compare with our results directly; therefore, it is difficult to say whether the results of this study fit a pattern seen in response to plastic exposure. However, bluegill BF-2 cells have been used in other ecotoxicological studies. Srikanth et al. (2018) used bluegill cells to investigate how graphene oxide exposure induced cytotoxicity and oxidative stress [70], and Poornavaishnavi et al. (2019) used bluegill BF-2 cells to assess oxidative stress, cytotoxicity, and morphological changes in response to nickel nanoparticles (Ni NPs) [71]. Both studies showed that cell toxicity was both dose- and time-dependent. An organismal study involving exposure of Mozambique tilapia (Oreochromis mossambicus) to Ni NPs also confirmed the results of Poornavaishnavi et al. (2019) [71] in the whole animal [84], suggesting that results obtained in vitro can be extrapolated to entire organisms and, therefore, natural populations.
Though the current study employs different methods to determine a different outcome, the effects of Ni NPs and graphene oxide show that increases in cell damage can be linked to toxicant dosage and length of exposure. Similar results were expected for the current study, as research into the impacts of nanoparticles within the cell, such as the Srikanth et al. (2018) study [70], showed adverse effects across a wide range of contaminants and organisms. Given the mixed results of the current study, further investigation is required to gain a clearer picture of the toxicological effects of plastic nanoparticles. While no conclusive evidence showed that methylation was dose- or time-dependent, the presence of nanoplastics did cause significant increases in methylation in several of the experimental cultures and should be further investigated to determine if these increases could also be dose- and/or time-dependent.
Transcriptional changes due to microplastic exposure have been studied in zebrafish [32,33], but the subject of how micro-/nanoplastics change methylation within the genome of teleost fish is decidedly lacking. However, it has been well documented that aquatic organisms of all kinds ingest microplastics at an alarming rate [38,85,86]. Bluegills in the Brazos River Basin, Texas, specifically—and their sister species, longear (Lepomis megalotis) sunfish—were documented ingesting micro- and macroplastics [85]. Peters and Bratton (2016) also found that sunfish in the two largest size classes (10.1–13.9 cm and ≥14 cm) had the highest ingestion frequency [85]. It is concerning that the larger fish seem to be ingesting plastics at a higher frequency, because egg production is exponentially linked to body length, with larger females producing the most eggs. Aberrant methylation levels have also been shown to cause serious damage in the form of changes in gene expression and/or genomic rearrangements [87]. Further, European seabass (D. labrax) larvae demonstrated a masculinization of the female gonadal tissue due to increased methylation in response to higher-than-normal temperatures [54]. It is unknown if a similar response would occur due to micro-/nanoplastic exposure; however, a similar reaction in fish larvae or even adult fish could have a detrimental effect on egg production or reproduction in general, which could lead to a trophic cascade.
Another notable finding within the current study was that nearly all the time groups across the four experiments did show increased methylation compared to their controls; although there was not always a significant difference, there was an increasing trend. The controls for each experiment also showed a trend of increased methylation as time progressed. DNA methylation is a sensitive process and can be altered by many natural and unnatural phenomena, such as temperature, improper nutrition, and pollutants. Though the media for each culture was changed daily, it is possible that with the cultures at full confluency at T2 and T3, there was a buildup of waste products that increased methylation within the controls and skewed methylation patterns in favor of a statistically insignificant result.
Fish at all trophic levels play an essential role within the ecosystem, and abnormally high methylation levels could hinder optimal cellular function. Physiological responses to microplastic exposure, such as diminished egg production, could further stress already burdened aquatic ecosystems, which could have far-reaching impacts even on human food supplies. It is also possible that problems encountered by aquatic organisms in response to the ingestion of microplastics could also manifest themselves in humans. In fact, Leslie et al. (2022) conducted a novel biomonitoring study looking for plastic particles ≥700 nm in whole human blood [88]. Between the 22 individuals sampled, the average concentration of quantifiable plastic particles was 1.6 μg/mL [88]. Another study by Wang et al. (2022) examined metabolic alterations in human cells in response to polystyrene nanoplastics, and it was found that 16.46% of the quantified proteins and 17% of the quantified metabolites displayed altered levels in response to the plastic particles [89]. As transcriptional changes leading to down-regulation of proteins can and do occur due to epigenetic alterations, it is entirely possible that epigenetic changes in response to the polystyrene nanoplastics could have led to the metabolic changes seen by Wang et al. (2022) [89]. A similar reaction to nanoplastic exposure would be expected in the bluegill epigenome; as this study explored only global methylation patterns and did not differentiate if any specific biomarkers were down-regulated, further investigation is warranted to determine if particular genes are also depressed in response to nanoplastic exposure.
Microplastics have a known anthropogenic impact on aquatic ecosystems and their inhabitants, so to ensure aquatic ecosystems remain healthy and functional and to understand the effects micro-/nanoplastics may even have on humans, it would be prudent to investigate how micro-/nanoplastics affect methylation in both fish and other organisms’ genomes further. Given the current study’s results, it seems that exposure to plastic nanoparticles does increase methylation within bluegill BF-2 cells. It cannot be said that this increase in methylation was dose- or time-dependent. Yet, there appeared to be an increase in methylation between nearly every experimental group and their controls, even though some were not significantly different. In future methylation micro-/nanoplastic studies, increasing the number of controls and experimental groups per time period would help negate the effects of any outlier cultures with increased methylation due to temperature fluctuations, waste product buildup, or other unforeseen factors. It would also be essential to look for physiological responses from the cells, such as cytotoxicity and oxidative stress, in response to plastic contamination.
However, as stated in the Materials and Methods Section, the dosages used in the present study were not environmentally relevant concentrations. Unfortunately, a decided lack of global standards exists for sampling and quantifying micro-/nanoplastics from environmental samples, making it difficult for researchers to use realistic dosages in experimental studies [90,91]. Even low concentrations of micro-/nanoplastics can cause an array of adverse effects [90]. As stated earlier, nanoplastics can cross biological barriers [20] and enter cells, where they can wreak havoc by creating reactive oxygen species to cause DNA damage, DNA methylation, and oxidative stress [64,65]. They can also cause mechanical damage to cellular machinery and impede normal cellular functions [65]. All this could lead to the development of serious diseases, such as cancer [92]. Future work in this area of study and other micro-/nanoplastic research would benefit from standardized methods for micro-/nanoplastic collection and quantification from environmental samples, which would allow both in vivo and in vitro studies to look at the effects of current plastic levels rather than hypothetical, arbitrarily chosen levels. Greater clarity of the risks posed by micro-/nanoplastics through improved standardization of collection and quantification methods and risk assessment methods would help everyone better understand the breadth of the problem, thereby allowing scientific communities, industries, policymakers, and society at large to know how and why to mitigate plastic pollution and consumption.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/microplastics4010010/s1, Figure S1: Restriction sites of restriction enzymes MspI and HpaII based on methylation state. Table S1: Sequences of adaptors and pre- and selective primers used for MS-AFLP. Table S2: Reagents and volumes used for both primer combinations, master mix A (MMA) and master mix C (MMC).

Author Contributions

Conceptualization: S.M.W. and A.M.J.; methodology: S.M.W., A.M.J. and J.M.W.; software: S.M.W.; validation: S.M.W. and A.M.J.; formal analysis: S.M.W., A.M.J. and J.M.W.; investigation: S.M.W.; resources: A.M.J.; data curation: S.M.W. and A.M.J.; writing—original draft preparation: S.M.W. and A.M.J.; writing—review and editing: S.M.W., A.M.J. and J.M.W.; visualization: S.M.W.; supervision: A.M.J.; project administration: A.M.J.; funding acquisition: S.M.W. and A.M.J. The first author, S.M.W., hereby claims that she performed literature reviews, carried out the methods of this experiment, analyzed the results, and drew conclusions based on previous research and empirical data. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University of West Florida Hal Marcus College of Science and Engineering Graduate Grant, University of West Florida Biology Department.

Institutional Review Board Statement

All research was conducted in accordance with the University of West Florida code of ethics.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

We thank Wayne Bennett (UWF) and Peter Cavnar (UWF) for their technical advice and the use of lab space for cell culture. We also thank the Hal Marcus College of Science and Engineering for providing funds to help support this study. We also thank Candice Lavelle (EPA) for her help in developing the methodology presented here and procuring the bluegill BF-2 cells.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Bar graphs showing the percentage of loci that were either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated and comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls for MMA; (A) shows the results for experiment I (low concentration); (B) shows the results for experiment II (medium concentration); (C) shows the results for experiment III (high concentration); (D) shows the results for experiment IV (bioaccumulation).
Figure 1. Bar graphs showing the percentage of loci that were either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated and comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls for MMA; (A) shows the results for experiment I (low concentration); (B) shows the results for experiment II (medium concentration); (C) shows the results for experiment III (high concentration); (D) shows the results for experiment IV (bioaccumulation).
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Figure 2. Bar graphs showing the percentage of loci that were either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated and comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls for MMC; (A) shows the results for experiment I (low concentration); (B) shows the results for experiment II (medium concentration); (C) shows the results for experiment III (high concentration); (D) shows the results for experiment IV (bioaccumulation).
Figure 2. Bar graphs showing the percentage of loci that were either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated and comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls for MMC; (A) shows the results for experiment I (low concentration); (B) shows the results for experiment II (medium concentration); (C) shows the results for experiment III (high concentration); (D) shows the results for experiment IV (bioaccumulation).
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Table 1. Total concentrations of nanoplastic particles for each plate in the three time periods for experiments I, II, III, and IV.
Table 1. Total concentrations of nanoplastic particles for each plate in the three time periods for experiments I, II, III, and IV.
ExperimentTime PeriodTotal Concentration (Particles)
I (Low Concentration)T1 (24 h)2.84 × 1011
T2 (48 h)2.84 × 1011
T3 (72 h)2.84 × 1011
II (Medium Concentration)T1 (24 h)5.68 × 1011
T2 (48 h)5.68 × 1011
T3 (72 h)5.68 × 1011
III (High Concentration)T1 (24 h)8.52 × 1011
T2 (48 h)8.52 × 1011
T3 (72 h)8.52 × 1011
IV (Bioaccumulation)T1 (24 h)2.84 × 1011
T2 (48 h)5.68 × 1011
T3 (72 h)8.52 × 1011
Table 2. Number of methylation-susceptible loci and non-methylated loci and the number of polymorphic methylation-susceptible loci (MSL) and non-methylated loci (NML) for each time group in each experiment for master mix A (MMA).
Table 2. Number of methylation-susceptible loci and non-methylated loci and the number of polymorphic methylation-susceptible loci (MSL) and non-methylated loci (NML) for each time group in each experiment for master mix A (MMA).
ExperimentNumber of MSLNumber of NMLNumber of Polymorphic MSLNumber of Polymorphic NML
NumberTime
IT1220 (78.6%)60 (21.4%)11614
T2211 (75.4%)69 (24.6%)814
T3199 (71.1%)81 (28.9%)695
IIT1206 (73.8%)73 (26.2%)830
T2198 (71.0%)81 (29.0%)801
T3189 (67.7%)90 (32.3%)642
IIIT1182 (65.5%)96 (34.5%)708
T2181 (65.1%)97 (34.9%)641
T3186 (66.9%)92 (33.1%)642
IVT1203 (66.3%)103 (33.7%)914
T2196 (64.1%)110 (35.9%)657
T3216 (70.6%)90 (29.4%)772
Table 3. Number of methylation-susceptible loci and non-methylated loci and the number of polymorphic methylation-susceptible loci (MSL) and non-methylated loci (NML) for each time group in each experiment for master mix C (MMC).
Table 3. Number of methylation-susceptible loci and non-methylated loci and the number of polymorphic methylation-susceptible loci (MSL) and non-methylated loci (NML) for each time group in each experiment for master mix C (MMC).
ExperimentNumber of MSLNumber of NMLNumber of Polymorphic MSLNumber of Polymorphic NML
NumberTime
IT1151 (65.1%)81 (34.9%)552
T2162 (69.8%)70 (30.2%)627
T3139 (59.9%)93 (40.1%)483
IIT1149 (60.1%)99 (39.9%)590
T2175 (70.6%)73 (29.4%)703
T3168 (67.7%)80 (32.3%)672
IIIT1144 (59.5%)98 (40.5%)582
T2160 (66.1%)82 (33.9%)600
T3162 (66.9%)80 (33.1%)617
IVT1185 (67.3%)90 (32.7%)802
T2171 (62.2%)104 (37.8%)554
T3189 (68.7%)86 (31.3%)631
Table 4. Percentage of loci of the experimental time groups T1, T2, and T3 and their respective control groups that are either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated using MMA; comparisons of the methylation state between controls and the respective experimental group that were statistically significant are in bold.
Table 4. Percentage of loci of the experimental time groups T1, T2, and T3 and their respective control groups that are either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated using MMA; comparisons of the methylation state between controls and the respective experimental group that were statistically significant are in bold.
Experiment NumberC1T1C2T2C3T3
INo Methylation17.73%18.38%21.33%13.74%17.09%13.15%
Hemimethylation10%10.97%14.69%10.16%10.55%11.47%
Internal Cytosine Methylation18.18%23.96%18.48%25.8%56.28%26.21%
Full Methylation54.09%46.69%45.5%50.3%16.08%49.16%
IINo Methylation31.55%18.12%31.31%17.53%15.34%13.68%
Hemimethylation7.77%13.11%12.63%10.89%13.23% 8.31%
Internal Cytosine Methylation28.16%23.22%16.67%23.74%20.11%25.25%
Full Methylation32.52%45.55%39.39%47.84%51.32%52.76%
IIINo Methylation21.15%16.58%25.41%14.36%26.34%14.58%
Hemimethylation12.09%9.43%7.74%13.26%5.38%10.22%
Internal Cytosine Methylation25.27%23.90%35.91%19.26%29.03%18.35%
Full Methylation41.48%50.09%30.94%53.12%39.25%56.85%
IV No Methylation35.47%18.37%15.82% 16.69%16.67%17.52%
Hemimethylation6.90%11.40%15.82%9.55%31.48% 16.59%
Internal Cytosine Methylation30.05%20.76%16.33%24.85%10.19%12.96%
Full Methylation27.59%49.47%52.04%48.91%41.67%52.93%
Table 5. χ2 values for experiments I, II, III, and IV comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls, as well as the different time groups to each other for MMA; below the diagonal, the χ2 statistic is provided for each comparison; above the diagonal, p-values for each comparison are provided, and bolded p-values are statistically significant.
Table 5. χ2 values for experiments I, II, III, and IV comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls, as well as the different time groups to each other for MMA; below the diagonal, the χ2 statistic is provided for each comparison; above the diagonal, p-values for each comparison are provided, and bolded p-values are statistically significant.
Experiment Number ControlT1T2T3
I (Low Concentration)Control 0.71410.27033.162 × 10−6
T11.364 0.82450.7908
T23.9190.904 0.9908
T328.2861.0430.109
II (Medium Concentration) Control 0.05390.10820.6104
T17.647 0.96430.5107
T26.0720.277 0.775
T31.8212.3091.109
III (High Concentration)Control 0.63030.00150.0147
T11.730 0.7070.7252
T215.3981.394 0.9092
T310.5121.3170.544
IV (Bioaccumulation)Control 0.00240.32960.0977
T114.449 0.89330.4067
T23.4330.614 0.121
T36.3052.9045.814
Table 6. Percentage of loci of the experimental time groups T1, T2, and T3 and their respective control groups that are either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated using MMC; comparisons of the methylation state between controls and the respective experimental group that were statistically significant are in bold.
Table 6. Percentage of loci of the experimental time groups T1, T2, and T3 and their respective control groups that are either unmethylated, hemimethylated, internal cytosine methylated, or fully methylated using MMC; comparisons of the methylation state between controls and the respective experimental group that were statistically significant are in bold.
Experiment NumberC1T1C2T2C3T3
INo Methylation15.23%15.14%16.98%16.68%20.86%13.07%
Hemimethylation17.22%10.79%22.22%14.03%12.95%16.91%
Internal Cytosine Methylation19.87%22.23%14.81%21.54%42.45%24.22%
Full Methylation47.68%51.84%45.99%47.75%23.74%42.80%
IINo Methylation34.23%20.02%29.71%19.59%17.26%16.50%
Hemimethylation16.11%16.33%13.14%13.14%21.43% 12.16%
Internal Cytosine Methylation30.87%25.62%29.71%21.63%14.88%19.98%
Full Methylation18.79%38.03%27.43%45.63%46.43%51.36%
IIINo Methylation23.61%18.29%28.75%15.62%28.40%15.28%
Hemimethylation15.97%17.36%23.15%22.86%9.88%14.89%
Internal Cytosine Methylation24.31%16.78%25.00%14.46%24.69%16.82%
Full Methylation36.11%47.57%23.13%47.05%37.04%53.01%
IVNo Methylation28.65%18.30%12.87% 16.21%8.47%13.84%
Hemimethylation14.05%15.37%24.56%17.29%41.80%20.46%
Internal Cytosine Methylation23.24%17.84%16.37%24.23%11.64%16.40%
Full Methylation34.05%48.49%46.20%42.27%38.10%49.29%
Table 7. χ2 values for experiments I, II, III, and IV comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls, as well as the different time groups to each other for MMC; below the diagonal, the χ2 statistic is provided for each comparison; above the diagonal, p-values for each comparison are provided, and bolded p-values are statistically significant.
Table 7. χ2 values for experiments I, II, III, and IV comparing time groups T1 (24 h), T2 (48 h), and T3 (72 h) to their respective controls, as well as the different time groups to each other for MMC; below the diagonal, the χ2 statistic is provided for each comparison; above the diagonal, p-values for each comparison are provided, and bolded p-values are statistically significant.
Experiment Number ControlT1T2T3
I (Low Concentration) Control 0.61870.37170.0053
T11.783 0.87880.4922
T23.1320.676 0.7799
T312.7152.4081.088
II (Medium Concentration) Control 0.01330.04850.3118
T110.726 0.71090.3037
T27.8831.377 0.8717
T33.5703.6350.706
III (High Concentration)Control 0.29780.00190.0258
T13.683 0.7680.8606
T214.8551.138 0.5281
T39.2780.7532.220
IV (Bioaccumulation)Control 0.13410.34170.0126
T15.577 0.65120.7034
T23.3431.636 0.4752
T310.8481.4092.500
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Wilkinson, S.M.; Whitaker, J.M.; Janosik, A.M. Investigating the Epigenetic Effects of Polystyrene Nanoplastic Exposure in Bluegill (Lepomis macrochirus) Epithelial Cells Using Methylation-Sensitive AFLPs. Microplastics 2025, 4, 10. https://doi.org/10.3390/microplastics4010010

AMA Style

Wilkinson SM, Whitaker JM, Janosik AM. Investigating the Epigenetic Effects of Polystyrene Nanoplastic Exposure in Bluegill (Lepomis macrochirus) Epithelial Cells Using Methylation-Sensitive AFLPs. Microplastics. 2025; 4(1):10. https://doi.org/10.3390/microplastics4010010

Chicago/Turabian Style

Wilkinson, Sheridan M., Justine M. Whitaker, and Alexis M. Janosik. 2025. "Investigating the Epigenetic Effects of Polystyrene Nanoplastic Exposure in Bluegill (Lepomis macrochirus) Epithelial Cells Using Methylation-Sensitive AFLPs" Microplastics 4, no. 1: 10. https://doi.org/10.3390/microplastics4010010

APA Style

Wilkinson, S. M., Whitaker, J. M., & Janosik, A. M. (2025). Investigating the Epigenetic Effects of Polystyrene Nanoplastic Exposure in Bluegill (Lepomis macrochirus) Epithelial Cells Using Methylation-Sensitive AFLPs. Microplastics, 4(1), 10. https://doi.org/10.3390/microplastics4010010

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