The Effect of Microplastics on Microbial Succession at Impaired and Unimpaired Sites in a Riverine System

doi:10.21203/rs.3.rs-4953194/v1

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Research Article

The Effect of Microplastics on Microbial Succession at Impaired and Unimpaired Sites in a Riverine System

https://doi.org/10.21203/rs.3.rs-4953194/v1

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Background

Studies into biofilms and interactions with anthropogenic substrates like microplastic polymers are predominantly represented in the literature concerning marine environments. Less is known about microplastics in riverine environments that feed the microplastic accumulation of marine environments, transporting potentially harmful or pathogenic organisms that have accumulated on the microplastics. Environmental nutrient loads, seasonality, and geography are all known to influence microbiome formation. This project compared the microbial diversity of biofilms that developed on microplastics to natural stone substrates in an impaired and unimpaired section of the Quinnipiac River Watershed. We evaluated microbial diversity and composition via 16S rRNA gene sequencing while monitoring total colony and fecal coliform colony counts using standard water monitoring methods.

Results

Total coliform colony counts were higher in the impaired Quinnipiac River than in unimpaired Honeypot Brook (W = 583, p = 0.037) and on the microplastic substrate than stone substrate (W = 1038, p = 0.022). Sequenced features to the class level were dominated by Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, comprising 75% of the community biome. Simpson’s Diversity indices indicated that within the two substrates, there was little variation in the features present. However, it was noted that microplastic alpha diversity trended slightly lower than the stone. Further analysis of common aquatic enteropathogens showed that the genera Citrobacter was significantly more abundant on the microplastics at both locations.

Conclusions

Our results indicate impaired waterbodies with a microplastic burden may retain greater fecal coliform bacterial loads than unimpaired waterbodies. Increased microplastic loads in compromised lotic systems may have an additive impact. Water quality remediation and careful monitoring are recommended to reduce this effect. Comparing this study with environmental community analysis could provide valuable insight into preferential surface attachment of bacteria onto microplastic.

microplastic

biofilm

16S rRNA gene

freshwater

pollution

coliform

riverine

Plastic debris and its cumulative environmental concerns, such as general accumulation from point and non-point sources, marine mammal entanglement and ingestion, and persistence due to variable degradation rates (Nawab et al., 2023; Yang and Wang, 2023; Stolte et al., 2022; Brandon et al., 2016) were first described in Zettler et al. (2013) as a ‘sphere’ of Earth. Surface colonization of microplastics by pioneer organisms begins immediately upon discharge into an environment (Rummel et al., 2017). The subsequent secretion of extracellular polymeric substances by bacteria that have adhered to the microplastics form a distinct biome that alters the topography and density of the microparticle as it ages (Kooi et al., 2017; Rummel et al., 2017; Fazey and Ryan, 2016; Lobelle and Cunliffe, 2011). The composition of the microplastic biome is of particular importance and can define the plastisphere, which differs in fresh and marine water, lacustrine, and riverine environments (Qiang et al., 2021; Zettler et al., 2013). Specific environmental conditions, such as nutrient levels, salinity, pH (Oberbeckmann et al., 2018), geography and seasonality (Marsay et al., 2023; Zhang et al., 2022), and the substrate surface itself (Rummel et al., 2017), are also integral in the selection for organisms that promote microplastic primary or secondary succession.

Freshwater studies that examine riverine microplastic burden are less abundant in the literature compared to those that are marine-associated (McCormick et al., 2016). Li et al. (2018) found that despite the limited research in this area, freshwater microplastic abundance is comparatively similar to marine waterbodies. Further, an estimated 80% of microplastics introduced into marine systems have provenance from land and river effluent (Wu et al., 2019). Like marine plastics, freshwater particle biomes preferentially select from the surrounding environment due to the novel substrate surface compared to natural substrates (Wu et al., 2019; McCormick et al., 2016). Using 16S rRNA gene sequencing, Wu et al. (2019) found that lab-cultured riverine microplastic biofilms shared similar phyla with rock biofilm, apart from Proteobacteria, Gemmatimonadetes, and Actinobacteria that were unique to the microplastic assemblage. Lower community richness and evenness were found on microplastics sampled in midwestern riverine wastewater treatment plant effluent (McCormick et al., 2016).

Notably, the potential for ecotoxicity from microplastic biofilms is still unclear. Three identified criteria influence the adherence of pollutants: sorption capacity of the microplastic and biofilm together, pollutant properties, and time spent in the environment (Kalcíková and Bundschuh (2021). Several studies have illustrated the dynamic adsorptive properties related to heavy metal accumulation, pharmacologic, and pathogen transport (Feng et al., 2020; Magadini et al., 2020; Richard et al., 2019; Li et al., 2018).

The Quinnipiac River Watershed encompasses 430 square kilometers (km²) of eight sub-watersheds that drain into a 38-mile urban river artery. The watershed originates in wetland Deadwood Swamp at the border of Plainville and Farmville, Connecticut, and terminates in New Haven Harbor (CT.gov, 2012). Point and non-point sources of pollution have historically been an issue. As of the 2010 EPA report, 75 miles of rivers and streams in the watershed were impacted by enterobacterial pollution, with 20.8 miles affected by polychlorinated biphenyl (PCB) compounds (CT.gov, 2012). The presence of known endocrine disrupters has been found in this waterway since 2022 (Eldridge and Simjouw, 2022). A 2020 Quinnipiac River Fund Final Report reported that the river discharges 272 million microplastics annually at the Meriden wastewater treatment plant and 72 million particles from the North Haven plant, with the greatest concentrations seen in decreasing temperatures (Breslin, 2020). At peak rainfall, microplastic concentrations increased by 3500% (Benoit, 2022).

Wastewater effluent is thought to be a significant source of microplastic discharge into a riverine system (McCormick et al., 2016). Further, colonization within the treatment system before discharge can protect the developing biomass as these organisms are not subjected to natural grazers (Kelly et al., 2021; Eckert et al., 2017). Human-associated pathogenic microorganism attachment has also been demonstrated to preferentially select microplastics after secondary and tertiary wastewater treatment by offering a favorable, low-biodegradable surface compared to organic particles (Kruglova et al., 2022). Because riverine systems typically receive wastewater discharge and the accompanying pollutants, it raises the question of whether microplastics have an additive effect in impaired waterbodies, increasing the potential for sequestration of harmful or pathogenic organisms.

This project aimed to examine microbial community differences in an anthropogenically-introduced substrate with that of stone typically found in the environment and the influence of waterbody impairment. It was hypothesized that the polypropylene microplastic will form a distinct biofilm different from that of the stone substrate at the impaired sampling site compared to the non-impaired site, thus increasing the role of microbiome development in polluted waters. We sought first to identify any preferential coliform colony accumulation on the microplastic substrate over the stone through selective culturing and, second, to assign microbial phylogeny to describe the substrate communities based on alpha and beta diversity indices.

Site Selection

Study locations were chosen based on water quality classification and human use criteria outlined in the Quinnipiac River Watershed 2012 water quality assessment (QRWA, 2013). Sites were either ‘fully impaired’ or ‘unimpaired’ and supported one or more of the following categories: recreational activities, aquatic life, or fish consumption. Honeypot Brook (Cheshire Park, 1000 Highland Avenue, Cheshire, CT 06140) was the unimpaired deployment site. The Quinnipiac River (Quinnipiac Park River Walk and Canoe Launch/Treatment Plant, 1325 Cheshire Street, Cheshire, CT 06140) served as the impaired deployment site (Fig. 1).

Cage Deployment and Biome Development

To test differences in biofilm development between microplastic and stone substrates, 10 individual replicate biomes were created by suspending substrate-filled, 3-inch, household tea infusers (Thunder Group®, City of Industry, CA 91748) wired shut with enameled floral wire (Hillman™, Tempe, Arizona, 85284). The infusers were suspended in two rows of 5, secured with a 150-pound strength fishing line (Reaction Tackle, Big Bend, Wisconsin, 53103). The cage from which they were suspended was constructed from schedule 40 PVC ½” – 1” piping assembled in a cube conformation and sand-weighted to rest on the sediment bed. This approach was similar to one that Magadini et al. (2020) employed, which allowed for constant water flow-through, and contact with, the substrates (Fig. 2).

Commercially made 3 mm polypropylene plastic pellets (Fairfield™ Poly-Fil, Danbury, Connecticut, 06810) were used as the ‘microplastic substrate’. The natural stone substrate was collected from the unimpaired Honeypot Brook location and sifted through stainless steel 4 mm and 2 mm field sieves to capture a 3 mm size comparable to the plastic pellets. Both microplastic and stone substrates were soaked in 70% isopropyl alcohol (Fisher Chemical, Fair Lawn, New Jersey, 07410) for 1 hour and dried thoroughly to ensure the initial substrate surface was disinfected. Each tea infuser was wet-weighed using sterile, deionized water on an analytical balance (Mettler Toledo, Cole-Palmer®, Vernon Hills, Illinois 60061) and then dried for 10 seconds. Two tablespoons of stone or microplastic substrate were added, dipped, and allowed to drip dry for 10 seconds. The resulting wet weight of the infuser and substrate was recorded. Five infusers of each substrate (10 total) were held as non-deployed controls.

Each tea infuser was notch-coded with a wire cutter according to sample site, substrate, and replicate number. Each set was identified on the cage using colored zip-ties. Two frames per site were tethered with zip-ties and submerged in the water column. The weighted frames were tied to reinforcing bars at the waterbody bank and identified with signage.

Weekly visits were made during deployment to ensure cages remained submerged and intact. Ten replicates from each substrate were harvested on days 30, 60, and 90 from May to August 2023 at each site. The samples were removed from the launch frame using nitrile gloves (Medline FitGuard® Touch, Northfield, Illinois, 60093), drip-dried for 10 seconds, and then individually weighed in clean weigh boats on a field analytical scale (OHAUS® Navigator™, Parsippany, New Jersey, 07054). Samples were placed in a Whirl-Pak sample bag (Whirl-Pak®, Fort Atkinson, Wisconsin, 53538) and then transferred to ice in a cooler before transport to the University of New Haven for biofilm harvest.

Water Quality and Sample Collection

Site water collection followed Method A, as described by the Environmental Protection Agency’s protocol for proper sampling (EPA, 2012). Three in-water meter readings were collected downstream of the deployed apparatus. Dissolved oxygen (DO) in milligrams/liter (mg/L) and salinity in parts per thousand (ppt) were measured via YSI Conductivity, Optical Dissolved Oxygen meters (YSI Pro30 Conductivity Meter; YSI ProODO Optical Dissolved Oxygen Instrument, YSI Inc., Yellow Springs, Ohio 45387). pH was measured with an Orion Star hand-held meter (Thermo-Fisher Scientific™ Orion Star A121, Waltham, Massachusetts, 02451).

A total of 18 water samples (6 per site x 3 sampling periods) were taken and processed for nitrate (YSI Nitratest, YPM163) in parts per million (ppm) and phosphate (YSI Phosphate LR, YPM177) in ppm analysis using a YSI EcoSense® 9500 Photometer (YSI Inc., Yellow Springs, OH 45387). Results were recorded in Microsoft Excel (Microsoft® Excel for Mac, version 16.82).

Selective Media Preparation and Plating

Replicate plates of Brilliance™ E. coli/fecal coliform selective media (Brilliance™ agar CM0956, Thermo-Fisher™ Scientific, Waltham, Massachusetts, 02451) were prepared using sterilized nanopure water (Barnstead E-Pure Water Purification System, Thermo-Fisher™ Scientific, Waltham, Massachusetts, 02451) per manufacturer's instructions. To control fungal overgrowth, the media was treated with Amphotericin-B (A4888-1G, Sigma® Life Sciences, St. Louis, Missouri, 63103) prepared as 0.1 g in 10 mL dimethylsulfoxide (BP231, Fisher Bioreagents, Thermo-Fisher™ Scientific, Waltham, Massachusetts, 02451), filtered sterilized through a 0.22 micrometer (µm) pore size filter (Millex®, Duluth, Georgia, 30097) and then added 1 mL/1 L of liquid media. Approximately 25 mL of sterilized media was added to Petri plates (Fisherbrand™, Thermo-Fisher™ Scientific, Waltham, Massachusetts, 02451) and dried under a laminar flow hood for 24 hours. Several plates were reserved for quality control testing with known Gram-positive and Gram-negative bacteria: Lactococcus lactis, Staphylococcus aureus, Clostridium sporogenes, Escherichia coli, Klebsiella pneumoniae, Alcaligenes faecalis, Salmonella typhimurium, and Enterobacter aerogenes. The plates were incubated at 30°C for 24 hours. No growth was observed on the Gram-positive streaked plates, which is expected of this media per manufacturer instructions. E. coli was confirmed to have dark purple colonies, while K. pneumoniae and E. aerogenes grew bright pink colonies; both results confirmed that the media was prepared correctly with appropriate chromatic changes.

Accumulated Biomass Harvest

The non-deployed control replicate and deployed replicate infusers were emptied within the Whirl-Pak bag, rinsed with the open tea infuser with 10 mL of sterile saline, and agitated for 30 s. A 1 mL aliquot of this ‘biomass solution’ was used for 10-fold serial dilutions and plating. From each dilution, 200 µL were plated on duplicate plates as detailed in similar water quality testing methodologies (Bai et al., 2022; Forster & Pinedo, 2016) and then agitated for six-quarter turns using 8–10 2 mm autoclaved borosilicate glass beads. The plates were incubated at 30°C (VWR™, Radnor, PA 19087) for a minimum of 12 hours and up to 24 hours for color development. Plates with heavy overgrowth were labeled ‘Too Numerous to Count’ and not included in the final dataset. Purple and pink colonies were counted using a wide-field stereo microscope. Results were recorded in an Excel spreadsheet.

Substrate DNA Extraction

The remaining 9 mL of biomass solution of each replicate was centrifuged for 15 min at 10,000 xg to pellet the sample (EppendorfⓇ Centrifuge 5804 R, Enfield, Connecticut 06082). The resultant supernatant was removed, and the pellet was placed in a -80 C freezer until the following day. After thawing at room temperature, DNA extraction was performed using the Qiagen© DNeasy PowerSoil® Pro Kit (Kit 384, Qiagen© LLC, Germantown, Maryland 20874) per manufacturer instructions with the modification of using 250 µL of the pelleted biomass solution in place of 250 µL of soil. One µL of the extracted DNA was analyzed for purity via A₂₆₀/A₂₈₀ absorbance ratio using a Nanodrop™ One^c Microvolume UV-Vis spectrophotometer (Thermo-Fisher Scientific™, Waltham, Massachusetts, 02451). The extracted DNA samples were banked in a freezer at -20°C until the study period concluded.

Library Preparation and Real-Time Basecalling

Extracted DNA samples were randomly thawed in batches according to harvest date and genomic libraries were generated using the Oxford® Nanopore 16S rRNA sequencing kit using Kit 9 chemistry (SQK-16S024, Oxford Nanopore Technologies, Oxford Science Park, UK, OX4 4DQ) per the manufacturer instructions using all recommended consumables and reagents. DNA was amplified via a Bio-Rad T100™ thermocycler (Bio-Rad, Hercules, California, 94547) per protocol cycling conditions. DNA quantification of the eluted samples was performed using a Qubit 4 fluorometer (Invitrogen by Thermo-Fisher Scientific ™, Waltham, Massachusetts, 02451) with the dsDNA HS Assay Kit (Ref. Q32851, Invitrogen by Thermo-Fisher Scientific™, Life Technologies Corporation, Eugene, Oregon, 97402). Samples with less than 200 nanograms (ng) of library DNA were repeated under a new barcode array. Up to 24 barcoded samples from the same plate were pooled and then frozen at -20°C.

Real-time basecalling and demultiplexing of each pool were performed on the MinION Mk1c sequencing unit (MC-115173, Oxford Nanopore Technologies, Oxford Science Park, UK, OX4 4DQ) using the FLO-MIN106 flow cell (R9.4.1, Oxford Nanopore Technologies, Oxford Science Park, UK, OX4 4DQ). The minimum desired target was 15,000 reads per barcode with a total pool read of 5 million. Sequencing runs for each pool were approximately 22–24 hours. Barcodes created in processing error, repeated, or had less than 200 ng of genetic material have been excluded. Pass/fail basecall data per pool can be found in Table S1. Concatenated fastq files and study metadata can be found on the NCBI repository under BioProject ID PRJNA1096657. All relevant analysis and plotting scripts can be found on GitHub.com, annegilewski/freshwater-microplastics

Downstream analysis was performed utilizing the MetONTIIME pipeline (Matoute et al., 2024) developed for long-read analysis in the QIIME 2 (Bolyen et al., 2019) environment with the Docker (version 4.27.1) (Merkel, 2014) container interface. Taxonomic identification was assigned using the BioProject 33175 (NCBI, PRJNA3317, 2008) reference database with the VSEARCH (Rognes et al., 2016) classifier. The maximum number of reads per sample was set at 15,000 with a base pair minimum of 1000 and a maximum of 2000 to ensure coverage of the 16S region. Confidence in the feature identity to define the phylum taxonomic level is 80%, for genus 95%, and for species 97%. (Wensel et al., 2022). De Novo clustering was set at 90% to identify the Family taxonomic level. The minimum consensus for a match was set at 70%, with a minimum alignment identity of 80%. The maximum taxonomic level for identity was set to 6 (the level of genus). The complete table of the modified MetONTIIME pipeline parameters is in Table S2. Due to the size of the fastq output files, pooled barcodes were batched up to six at a time. Absolute frequency feature tables were separated by taxonomic level 2–6 corresponding to Phylum through Genus. These tables were then merged and filtered in the QIIME2 command line. Features that contained > 10 frequency hits across samples and were present in > 3 samples were retained for analysis. All plots were prepared in R (version 4.2.2) (R Core Team, 2022) and RStudio (version 2022.7.2.576) (RStudio Team, 2022) using the following packages: dplyr (Wickham et al., 2023), tidyr (Wickham et al., 2024), ggplot2 (Wickham, 2016), ggpubr (Kassambara, 2023), and stringr (Wickham, 2023).

The total number of samples for this study was 140: 120 deployed samples, 10 non-deployed substrate controls, and 10 empty infuser controls. Following sequencing data filtration described above, the final dataset represented 101 16S rRNA gene sequenced microbiome samples (impaired = 51, unimpaired = 50; microplastic = 49, stone = 52). For further analysis, any rare features that showed up in fewer than 3 samples with fewer than 10 total reads were removed to mitigate potential amplification and sequencing errors.

Water Sampling

All samples taken during the study period were “dry”, meaning there was less than 0.1” -2.0” of precipitation in the previous 96 hours (EPA, 2008). Average temperatures at both sites were identical, with comparable salinity values. The pH remained static at the unimpaired Honeypot Brook site (HP) (~ 6.6), while the impaired Quinnipiac River site (QR) experienced a peak in July (~ 7.1). Dissolved oxygen was also consistently lower at the impaired site than unimpaired, with the lowest value (~ 85%) at the D60 sampling point. Nitrate and phosphate of both sites were within a similar range, except for a possible phosphate spike at the unimpaired site at D60. Four of the six samples tested out of range (> 4.0 ppm), possibly due to poor water column mixing at those sample sites or from an incidental pulse dose from a fertilization application from the surrounding neighborhood. A summary table of the averaged water quality sampling data can be found in Table S3.

Microbiome Community Assemblage

Taxonomic distribution by class was visualized for the top 10 most abundant organisms per sample by percent relative abundance for site and substrate. (Fig. 3). Among the 87 classes of bacteria present, 70% of the reads were represented by the Pseudomonadota phyla; Alphaproteobacteria (µ = 26.3%), Betaproteobacteria (µ = 28.4%), and Gammaproteobacteria (µ = 15.3%).

Other noted findings are the rise of Bacilli and subsequent decrease in Planctomycetia in twelve samples (D30, n = 2) and (D60, n = 10) at the unimpaired location on both microplastic (n = 8) and stone (n = 4). Additionally, Clostridia was found in three samples (2 microplastic and 1 stone) and present at each sampling point.

Alpha and Beta Diversity

Standard Simpson’s diversity indices were created in the QIIME2 command line for the family taxonomic level. Using a jitter plot to visualize index clustering on the microplastic and stone substrates with respect to the site, the unimpaired site microplastic samples demonstrated a broader range of diversity than the impaired site microplastic samples. Stone sample indices were more tightly clustered than microplastics’, suggesting more similar richness and abundance, and had a mean diversity index that trended higher (Fig. S1). A Bray-Curtis dissimilarity analysis, also at the family taxonomic level visualizing the combination of site and substrate, showed minimal separation suggesting similar communities across samples (Fig. S2).

Plated Media

Nine of the ten non-deployed substrates showed no coliform growth after 24 hours, indicating the absence of coliforms on the stone and microplastic substrates before starting the study period. At the D30 and D60 harvest, we noted significant issues with selective media counting owing to overgrowth at 1:1, 1:10, and 1:100 dilutions, particularly in the pink non-E. coli coliform colonies. D90 samples were diluted to 1:1000 and provided the most accurate counting data.

For final analysis of fecal coliform growth, final D90 samples were used to represent the final succession of bacterial communities (n = 80). Shapiro-Wilks testing confirmed a non-normal distribution for total coliform colonies (W = 0.84, p = 9.53e-08). Mann-Whitney U testing revealed significantly greater total colony counts at the impaired site compared to the unimpaired site (W = 583, p = 0.037). There were also greater total colonies in microplastics compared to stone substrate (W = 1038, p = 0.022). A Poisson generalized linear model was applied using the lme4 package (Bates et al., 2015) for mixed-effect modeling, using site and substrate as predictors and total colonies per gram of substrate as the response. Four different a priori models were created. Akaike information criterion (AIC) was then used to rank the models, resulting in the selection of the site + substrate model. Model goodness-of-fit was evaluated with a likelihood ratio test (LRT) against the null model. All models were determined to be sound (Table S4). Further assessment of confidence intervals was set at 95%, indicating that none of the top-ranked models overlapped 0 (Table S5). The resulting box plot showed that a significant increase (p < 0.01) in the microplastic substrate at the impaired site as well of the stone in the unimpaired site. An additional significance (p < 0.05) towards colonies on the microplastic versus stone within the impaired site (Fig. 4).

Further exploration of the colony distribution was performed on the D90 subset by filtering the Enterobacteriaceae genus, along with other genera known for having pathogenic organisms, such as Vibrio, Aeromonas, Salmonella, Shigella, Clostridium, and Legionella, by percent relative abundance (Fig. S3). Mann-Whitney U statistical analysis (p < 0.05) was performed to determine the significance of the median difference of these organisms based on substrate type. The results were visualized via a Cleveland Dot plot in R. Aeromonas and Citrobacter counts were significantly more abundant (p < 0.05) on the microplastic whilst Clostridium and Legionella were more significant on the stone. Enterobacter also demonstrated greater relative abundance on the microplastic but was not significant. (Fig. 5).

The results indicate that the microbiome communities had similar composition and class taxon accumulation across the study period, with minimal differences in alpha and beta diversity. Total plated coliform counts were in greater abundance at the impaired (QR) and on the microplastic (MP) substrate. 16S rRNA gene sequencing of genera of human disease concern indicates that both substrates do have the potential to harbor distinct pathogens in either site.

This study aimed to analyze the difference between anthropogenic microplastic and natural stone substrates normally present in the environment to examine potential differences in the microbial communities that formed over 90 days in a freshwater riverine system. An additional variable of site impairment was added to evaluate the influence of water body health on microbiome development. Plated coliforms served as the metric to track microbiome adhesion over time and compared to the sequencing data to identify the presence or absence of potentially harmful organisms. Previous testing on this region of the Quinnipiac River used membrane filtration methodology for colony culturing (Mitch et al., 2010) using E.coli as the fecal indicator. With the advent of next-generation sequencing, these indicator species have broadened, and analyses can determine point sources and chronic conditions (McLellan and Eran, 2014). Our study demonstrates that 16S rRNA analysis in freshwater riverine systems provides greater resolution of microplastic microbiome attachment beyond E.coli and that other enteropathogens may be in higher abundance, thus necessitating the inclusion of metagenomic sequencing in water quality monitoring.

The sites selected for this study were purposefully chosen based on their waterbody health status to analyze the impact of site on coliform accumulation. The impaired Quinnipiac River location is in proximity to one of three upriver wastewater treatment plants (Cheshire, CT) along the main river artery. Conversely, the unimpaired Honeypot Brook location is a tributary of the artery fed by surface and groundwater. We found no significant difference in site location for temperature and salinity of the water quality indicators that influence biome development (Oberbeckmann et al., 2018; Marsay et al., 2023). Of the other parameters, dissolved oxygen was lower during the study period, and an elevated D60 pH level was noted at the impaired site. However, with limited data, we cannot speculate on seasonal influence as our study was conducted over the summer months, but future directions could compare winter and summer sampling periods.

Microbiome analysis from 16S rRNA gene sequencing showed that three Proteobacteria classes (Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria) were abundant across site and substrate. Wu et al., 2019 found similar percentages (60–77%) when comparing lab-cultured riverine microplastic and stone biofilms. Alphaproteobacteria and Gammaproteobacteria have been shown to establish the microbiome early in the colonization process in marine environments (McCormick et al., 2016; De Tender et al., 2015). Plastic type can also be influential in enriching these classes (Qiang et al., 2021). Similar colonization assemblage has been observed in fresh and waste riverine water where Gammaproteobacteria were more abundant on polyethylene and Betaproteobacteria on polystyrene; however, differences in the representation was theorized to be due to the surrounding environmental flora rather than polymer type (Parrish and Fahrenfeld, 2019).

When looking at the total coliform counts, the impaired site and microplastic substrate showed higher median counts than the unimpaired and stone substrate. However, based on the generalized linear model analyses, both site and substrate appear to additively have the most significant influence on coliform adhesion, suggesting a co-influential action. A laboratory-simulated model using differing concentrations of microplastic substrate and wastewater-treated effluent mixed with fresh riverine water found that the surrounding water heavily influenced the microplastic biofilm community (Eckert et al. (2018). Further, researchers found that higher concentrations of microplastics may drive colonization through the probability of contact with a new particle or through quorum sensing, signaling nearby organisms to attach. As such, a higher microplastic burden in a waterbody would offer more opportunities to colonize these particles. Qiang et al. (2021) found similar evidence of habitat influence in another laboratory-cultured study of the Raritan River in New Jersey, a tidally influenced river similar to the Quinnipiac River Watershed. In this study, microplastic biofilm communities differed in freshwater and estuarine sections of the same river, underscoring the influence of site-specific colonization.

Our alpha diversity indices suggested lower diversity in microplastic substrate over stone, which aligns with similar studies looking at artificial and natural substrates wherein lower diversity can be attributed to critical factors in microplastic microbiome selectivity, including the polymer type and pioneer colonization (Miao et al., 2019; McCormick et al., 2016). However, field-collected microplastics from river estuaries in the Mediterranean Sea and the Atlantic Ocean determined that marine community composition is also primarily geographically influenced; further, these researchers found no common “core” of organisms in the biofilm and concluded that each ‘plastisphere’ should be considered a separate ecosystem, unique from one another (Marsay et al., 2023).

Reviews of opportunistic infections by water-borne or water-based organisms underscore the global threat of acquiring these diseases from impaired freshwater systems (Stec et al., 2022; Borque and Vinetz, 2018). This particular section of the river and others within the watershed are unsuitable for recreation, fishing, or sustaining aquatic life. Based on plated results, we did confirm the presence of fecal coliforms in higher abundance on the microplastic substrate at the impaired (QR) site. However, these data only present a generalized picture of waterbody health. Here, through sequence analysis of D90 genera from the Enterobacteriaceae class, we confirmed that the genera Escherichia were not a significant driver of biomass accumulation (Fig. S3). Interestingly, non-Escherichia genera were more abundant, suggesting that expanding analyses to all species in the Enterobacteriaceae genera is warranted. Additionally, the results showed significantly increased relative abundance for several other pathogenic genera. Wastewater-enriched systems host a wide diversity of organisms, many of which can survive the treatment processes (Varela and Manaia, 2013); the presence of these non-fecal genera further supports high-resolution sequence methodologies.

Research into freshwater riverine systems is significant due to their proximity to urban areas and the services provided through transport, wastewater discharge, and maritime or recreational activity. The socio-economic disparities in regions of pollution burden tend to favor wealthier communities at the expense of the poor: the so-called “luxury effect” (Schell et al., 2020). These affluence gaps are linked to uneven distributions of abiotic stressors, floral and faunal diversity, and pressure from pollutant sources. A review of 122 US urban Green Infrastructure plans showed that 80% omitted or poorly defined terms concerning ‘equity’ and ‘justice’. A meager 6% contained best practices in procedures that ensure equitable distribution of environmental services. By comparing biofilm assemblage in an impacted riverine section with an unimpacted section, we demonstrated the potential disparate effect of water quality and the influence of microplastic burden that could be present in a freshwater urban and non-urban riverine system (Grabowski et al., 2023).

At the D60 harvest date, it was noted that the sample cage at the unimpaired Honeypot Brook location was removed from the brook. A previous visit a week prior confirmed the position, and it was unclear how long the cage had been out of the water. The cage was replaced in the brook and allowed to soak for several minutes to rehydrate potentially desiccated biomass. The extent of biofilm disruption due to remaining out of the brook is unknown, though sequencing data did not indicate any significant reduction in feature counts.

It is also suspected that by D90, the accumulated detritus on the exterior of the infuser may have reduced flow-through to the substrate itself; this would have affected both substrates similarly. Alternatives for future studies include employing a less fine mesh containing the 3 mm substrate size, allowing for uninhibited water flow.

Future directions for this study include additional exploration of community richness and evenness over time, which could be an interesting focus. As Qiang et al. (2021) described, the dominant taxon was distinct from primary colonizers in the first 18 days of lab incubation, with a leveling off from days 18 to 31. Though outside of the scope of our analyses, a similar observation using Pielou’s evenness suggested significant change within groups between D30 and D60 (p = 6.13e07) (Fig. S4). Examination of successional growth over an extended study period may provide more information into early and later biofilm development and the influence of seasonal variability. Comparing environmental communities from water samples (Marsay et al., 2023; Qiang et al., 2021) with that of the adhered community to the microplastic substrate would be another approach to analyze the incident of preferential selection.

The limitation of selective media for coliform monitoring is that the methodology indicates the presence or absence of E.coli and non-E.coli organisms. While we did not see a significant abundance of E.coli in the substrate microbiomes, it was evident that the latter was more problematic and too general to determine what fraction of these organisms may be pathogens. Although more costly, using 16S rRNA gene sequencing elucidated the specific genera included in this category. The microbiomes of the microplastic and stone substrate were not overwhelmingly distinct at the class level as first hypothesized; however, there appears to be support that an impaired waterbody with microplastics may suffer from an additive impact of their presence. That is, the discharge of these particles in riverine systems alone does not address the issue's totality; the system's condition must also be considered.

Microplastic biomes present a complicated environmental issue that has the potential to be more impactful in impaired urban riverine ecosystems. The results of this study demonstrated a paired interaction of site and microplastic substrate concerning coliform attachment and adhesion of known pathogenic organisms. Given the importance of these waterways for communities, particularly those near polluting sources, improving water quality should be a top priority.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable

Availability of data and material

The datasets generated and/or analyzed during the current study are available in the NCBI BioProject ID repository, PRJNA1096657. Additional scripts can be found on GitHub.com, annegilewski/freshwater-microplastics

Competing interests

The authors declare they have no competing interests.

Funding

The Community Foundation for Greater New Haven (#20231327) generously supported the completion of this project.

Author Contributions

ALG designed and implemented the project and was the manuscript's first author. SS performed field and laboratory analysis as a research assistant and prepared Figure 1 for this manuscript. SNK advised ALG on field protocols and was a major contributor to writing the manuscript. NMS advised ALG on laboratory protocols and data analysis and was a major contributor to writing the manuscript. All authors have read and approved the final manuscript.

Acknowledgments

Enormous gratitude is given to Dr. Jean-Paul Simjouw, who served as a key advisor to this project, and John Kelley of the University of New Haven Schaub Makerspace for his guidance in creating the field cages. The Community Foundation for Greater New Haven generously funded this study.

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You are reading this latest preprint version

The Effect of Microplastics on Microbial Succession at Impaired and Unimpaired Sites in a Riverine System

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

INTRODUCTION

MATERIALS AND METHODS

Site Selection

Cage Deployment and Biome Development

Water Quality and Sample Collection

Selective Media Preparation and Plating

Accumulated Biomass Harvest

Substrate DNA Extraction

Library Preparation and Real-Time Basecalling

RESULTS

Water Sampling

Microbiome Community Assemblage

Alpha and Beta Diversity

Plated Media

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1