Unveiling microbial succession dynamics on different plastic surfaces using WGCNA

Keren Davidov; Sheli Itzahri; Liat Anabel Sinberger; Matan Oren

doi:10.1371/journal.pone.0318843

. 2025 Feb 6;20(2):e0318843. doi: 10.1371/journal.pone.0318843

Unveiling microbial succession dynamics on different plastic surfaces using WGCNA

Keren Davidov ¹, Sheli Itzahri ¹, Liat Anabel Sinberger ¹, Matan Oren ^1,^*

Editor: Arga Chandrashekar Anil²

PMCID: PMC11801547 PMID: 39913363

Abstract

Over recent decades, marine microorganisms have increasingly adapted to plastic debris, forming distinct plastic-attached microbial communities. Despite this, the colonization and succession processes on plastic surfaces in marine environments remain poorly understood. To address this knowledge gap, we conducted a microbiome succession experiment using four common plastic polymers (PE, PP, PS, and PET), as well as glass and wood, in a temperature-controlled seawater system over a 2- to 90-day period. We employed long-read 16S rRNA metabarcoding to profile the prokaryotic microbiome’s taxonomic composition at five time points throughout the experiment. By applying Weighted Gene Co-expression Network Analysis (WGCNA) to our 16S metabarcoding data, we identified unique succession signatures for 77 bacterial genera and observed polymer-specific enrichment in 39 genera. Our findings also revealed that the most significant variations in microbiome composition across surfaces occurred during the initial succession stages, with potential intra-genus relationships that are linked to surface preferences. This research advances our understanding of microbial succession dynamics on marine plastic debris and introduces a robust statistical approach for identifying succession signatures of specific bacterial taxa.

Introduction

Plastic pollution has become a global environmental concern, with far-reaching consequences for ecosystems, wildlife, and human health. Since its mass production began in the mid-20th century, plastic has transformed industries and daily life due to its durability, versatility, and cost-effectiveness. However, these very properties that make plastics invaluable have also led to their persistence in the environment. Each year, an estimated 8 to 12 million tons of plastic waste enters the oceans [1], where it accumulates, breaks down into microplastics, and infiltrates food webs [2, 3]. This accumulation not only threatens marine biodiversity but also carries potential risks to human health through bioaccumulation and trophic transfer [4].

Within minutes of entering the marine environment, plastic surfaces undergo colonization by marine bacteria [5]. Microbial colonization initiates a succession process marked by the formation of an initial biofilm that matures into a diverse community which includes phototrophs, heterotrophs, predators, decomposers, and symbionts [6]. This unique ecosystem, termed "plastisphere," supports biodiversity by offering a substrate for colonization in nutrient-poor open waters and accordingly possesses a distinct taxonomic composition compared to the surrounding water [7]. The plastisphere allows certain microbial species to thrive, including those capable of metabolizing hydrocarbons and potentially degrading plastics. On the other hand, the plastisphere also enables the transport of invasive species and pathogenic microorganisms across vast distances, disrupting local ecosystems [8]. When plastisphere biofilms mature, they can influence the buoyancy of the floating plastic debris [9], affecting how they disperse and accumulate in the marine environment.

Understanding the temporal dynamics and processes driving microbial community assembly and succession is crucial for elucidating the structure, function, and interactions within these communities over time [10]. Investigating these processes within the plastisphere poses unique challenges due to the influence of variable environmental factors, including geography and season [11–14], immersion/incubation time [15, 16] and salinity [17]. Another variable to be considered is the composition of the plastic surface itself. Plastics are manufactured from diverse polymers, each possessing distinct densities and potentially containing various fillers and additives. Moreover, plastic debris within the ocean undergoes continuous weathering, influenced by light, UV radiation, mechanical forces, and biological factors [18]. Previous studies indicate that the state of plastic weathering significantly impacts the composition of the surface microbiome [19, 20]. Conversely, understanding the influence of plastic polymer types on microbiome development and composition has been challenging and often inconclusive [15, 21–23]. While some studies suggest polymer-related variations in the initial marine plastic microbiome [24–29], statistically robust colonization preferences and succession signatures of specific taxa within naturally formed biofilms are lacking.

In this study, we aimed to reveal the impact of polymer type on the colonization and succession dynamics of marine bacteria. We used pristine plastic pellets of four common plastic polymers: Polyethylene (PE), Polypropylene (PP), Polystyrene (PS), and Polyethylene Terephthalate (PET). Additionally, we included non-plastic beads (glass and wood) for reference. These pellets and beads were immersed in a controlled seawater system which was pre-inoculated with plastic-associated microbiomes from the Mediterranean Sea. Employing a straightforward experimental design, we utilized a MinION nanopore-based, long-read 16S rRNA metabarcoding pipeline, accompanied by a range of complementary data analyses, to explore trends in microbiome biodiversity and composition across the different surfaces. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to the barcode sequence datasets to identify bacterial genera exhibiting significant surface- and time-specific succession signatures, as well as potential intra-genus relationships.

Material and methods

Experiments setup

The experiments took place in a 70-liter semi-sterile seawater aquarium with precise temperature control achieved through a chiller and a heater. The initial experiment, referred to as the ‘primary’ experiment, was conducted at 28 ± 2°C, reflecting the average summer temperatures of the Israeli Mediterranean Sea coastal water (https://www.israelweather.co.il). Subsequently, a second experiment explored the temperature effect on the microbiome composition and was conducted at a higher temperature of 35 ± 2°C, reflecting extreme conditions that may exist in shallow tidal pools during the summer [30]. Throughout both experiments, water salinity remained at approximately 40 parts per thousand (ppt), matching the typical salinity of the Eastern Mediterranean Sea (EMS) [31].

The aquarium was partitioned into two sections by a semipermeable barrier, facilitating water circulation while preventing the transfer of large items between compartments (S1 Fig in S1 File). Before being filled with sterile artificial seawater, the aquarium, the barrier and additional parts were cleaned and sterilized with 6% sodium hypochlorite (bleach). Plastic-associated marine bacteria were inoculated into the aquarium by introducing plastic marine debris collected in Herzliya Marina, as detailed below. Six days after the introduction of the marine plastic debris, new surfaces were added to the left compartment of the aquarium, where water flowed back to the sump. These surfaces consisted of organza mesh bags made of polyester (MO45600, Apath international), 6 x 8 cm in size, with mesh holes size of approximately 0.2–0.4 mm. Each of the bags was filled with 7 grams of pellets/beads (3–5 mm in diameter) made of either pristine (pre-processed, pure) PE (Ipethene^®, Carmel Olefines), PP (CAPILENE^® W 77 AV, Carmel Olefines), PS (Styrolution^® PS 124L, INEOS Styrolution), or PET (CZ-328, ZADE) plastic polymers, along with glass (5mm, 104017, Sigma) and wood (6 mm, LYS0118, Ningbo Linkyo). Before use, all materials were soaked in 70% ethanol for sterilization, followed by washing with sterile distilled-deionized water (DDW). The mesh bags, including replicates for each material according to the number of sampling time points, were suspended in mid-aquarium water by thin fishing lines tied to the aquarium top cover (S1 Fig in S1 File). In the second experiment, conducted at a higher temperature, we focused on PE, PET, glass, and wood to analyze the changes in the developing microbiome composition. All conditions, except the temperature, were similar to those of the primary experiment.

Inoculation and sampling

To initiate the inoculation of plastic-associated marine bacteria, we collected various plastic debris items, including debris of cups, bags, straws, plastic cutlery, and bottles, from the water of Herzliya Marina (32° 09′ 38.8" N 34° 47′ 35.0" E). The debris was collected on 20 July 2021 using a sterile aquarium net. At the time of collection, the water salinity was 40 ppt, and the water temperature was ~29.2°C. The debris was transferred with sterile tweezers into sterile glass vials containing filtered artificial seawater (FASW) that were kept in cooled Styrofoam boxes until reaching the lab. The collected debris underwent three washing steps with FASW to remove un-attached material. After washing with FASW, the items were submerged in the aquarium compartment, receiving a continuous inflow of sump-filtered seawater (S1 Fig in S1 File).

For DNA extraction, pieces from four plastic debris items (D1-D4 in the primary experiment and D5-D8 in the second experiment) were sampled on the experiment onset. Sampling of the newly introduced pellets/beads took place at specific intervals: 2, 7, 14, 30, and 90 days from the beginning of the primary experiment and 7, 14, and 30 days from the start of the second experiment.

At each designated time point, one organza bag containing pellets/beads of each tested material and half a liter of the surrounding aquarium water were sampled. Before further processing, the pellets/beads underwent thorough washing in FASW, to remove un-attached bacteria. The water samples were filtered through a 0.22 μm polyethersulfone membrane using a 20 L/min pump (MRC). Subsequently, DNA extraction was conducted from both the pellets/beads and the membranes following the procedure described below. All procedures were conducted inside a laminar hood.

DNA extraction

Pooled pellets/beads of each tested substrate, fragments of plastic debris, and membranes containing water filtrate underwent DNA extraction using the phenol-chloroform method. The extraction process was carried out in 15 ml tubes containing approximately 0.4 grams of sterile glass beads (G9268, Sigma) ranging in size from 425–600 μm, along with 3 ml of lysis buffer (composed of 10 mM Tris—HCl pH 8, 25 mM Na2EDTA pH 8, 1% v/v SDS, and 100 mM NaCl). Samples underwent bead beating, followed by digestion with proteinase K (5 units/μL) and Lysozyme (2000 units/μL). Subsequent DNA extraction steps followed the protocol outlined in reference [32]. The eluted DNA was collected in 40 μL EB (10 mM TE Tris 1 mM EDTA pH 8). For wood samples, DNA extraction utilized the DNeasy PowerWater Kit (Qiagen) following the provided protocol to maximize DNA yield. All DNA samples were stored at -20°C for subsequent analysis.

Fourier-transform infrared spectroscopy (FTIR)

Pieces of the plastic items (D1-D8) retrieved from the environment underwent Fourier Transform Infrared (FTIR) analysis to determine their polymer composition (S2 Fig in S1 File). The FTIR analysis was conducted using a JACSOFT/IR-6800 spectrometer, with spectra collected in the wavelength range of 200 cm^-1 to 4000 cm^-1 and a fixed resolution of 2 cm^-1. Each sample’s small plastic fragment was pressed under the K/Br crystal apparatus for measurement. The absorbance spectra were analyzed using the Open Specy open-source library [33].

MinION library preparation and multiplexed nanopore sequencing

Sequencing runs were executed across three MinION flow cells in the primary experiment and one flow cell in the second experiment, accommodating 12–22 multiplexed libraries per run. Library preparation utilized the 16S barcoding kit (SQK-16S024, Oxford Nanopore Technologies) following the manufacturer’s guidelines. For PCR amplification, 10 ng of genomic DNA from each sample was employed with barcoded primers targeting the full-length 16S rRNA gene (27F: 5′-AGAGTTTGATCMTGGCTCAG-3′ and 1492R: 5′-TACGGYTACCTTGTTACGACTT-3′). All PCR procedures included PCR blanks to detect potential foreign DNA contamination in the water, reagents or primers. Subsequently, barcoded libraries were pooled, loaded onto MinION flow cells (FLO-MIN106D R9.4.1), and sequenced for 16–24 hours, with base-calling automated through the MinKnow program. Raw reads were acquired in both FAST5 and FASTQ formats, with only "pass" quality reads selected for subsequent analysis. All Nanopore MinION filtered reads analyzed in this project have been deposited in the NCBI SRA database under BioProjects PRJNA1005105 and PRJNA1012293.

Data analysis

The base-called reads underwent processing using Nanopore version 2.1.0 and the 16S workflow v2023.04.21–1804452, accessed through https://epi2me.nanoporetech.com, via the EPI2ME desktop agent (ONT). The pipeline involved several steps: sorting reads into separate libraries based on multiplexing barcodes, trimming barcode and adaptor sequences, and comparison against the NCBI bacterial 16S database. Reads falling outside the range of 800 bp to 2000 bp in length were filtered out. Subsequently, the resulting CSV files from each EPI2ME run were downloaded and further processed using R (V4.3.1). Processing included merging EPI2ME 16s CSV outputs from individual MinION runs into a single file (pertaining to the primary experiment). Reads not assigned to a barcode and those lacking definitive classification (LCA≠0, either unclassified or belonging to more than one genus among the top three classifications) were filtered out. Multiplexing barcodes were converted into their corresponding samples, and read counts were normalized to relative abundances (percentages).

Diversity analysis

Alpha diversity parameters were obtained using MicrobiomeAnalyst [34] based on taxonomic 16S metabarcoding datasets at the species level. Utilizing the mapped read counts of the samples (S1 Table in S1 File) and observing species richness rarefaction curves (S3 Fig in S1 File), we standardized all samples to 7,940 mapped reads per sample. Community complexity within samples (alpha diversity) was evaluated using Chao and Shannon indexes to measure richness and diversity, respectively. Analyses were conducted based on sample type (combining all time points) and across time points (combining all surfaces) within the primary experiment. To assess dissimilarity in microbiome composition among samples (beta diversity), Principal Component Analysis (PCA) and Non-Metric Multidimensional Scaling (NMDS) with Bray-Curtis Dissimilarity Distances were employed. The PCA plot was generated using the PCAtools package (R package version 2.12.0, available at https://github.com/kevinblighe/PCAtools). An ANOVA test with post hoc pairwise comparison tests was performed to identify significant differences between the groups.

WGCNA analysis

The WGCNA analysis was performed using the WGCNA R package v1.71 [35]. Hierarchical clustering was then performed on the samples, and samples with a cut height below 7000 were retained (S4A Fig in S1 File). Another filtering round was performed to remove bacteria with low variance (goodSamplesGenes (GSG) function within the WGCNA package). For the network topology analysis (TOM), soft-thresholding power β = 10 was chosen (S4B Fig in S1 File). First, a similarity matrix between each pair of bacteria across all samples was created. The similarity matrix was then transformed into an adjacency matrix, and the topological overlap matrix (TOM) along with the corresponding dissimilarity (1-TOM) value were calculated. Next, module eigengenes were calculated, and module-trait associations were quantified. Finally, the membership strength of individual species to their module and their significance to the relevant traits of that module was assessed. This allowed us to identify bacteria with relative preferences of surface type and time.

Co-occurrence network visualization

The co-occurrence network analysis visualization was conducted using MicrobiomeAnalyst [34] with specific filter parameters: a minimum count of 20 and a mean abundance value of 30 (mbSet <- ApplyAbundanceFilter (mbSet, "mean", 20, 0.3)), and a low variance filter parameter of 10% (mbSet <- ApplyVarianceFilter(mbSet, "iqr", 0.1)). Only samples from the plastic microbiome were included in the analysis. The SparCC algorithm [36] was applied with a p-value threshold of 0.05 and a correlation threshold of 0.7 to identify significant co-occurrence patterns among microbial taxa.

Results

Metabarcoding outcomes and microbiome diversity

In total, 58 samples underwent sequencing, with 35 from the primary experiment, 15 from the second experiment, and eight from plastic debris (D1-D8). On average, each sample generated 55,378 reads (STDV = 32,465), of which an average of 36.4% were confidently mapped to the database in the primary experiment (with three top hits showing a single taxon, S1 Table in S1 File). The average read size across all samples was 1491.5 nucleotides (SDV = 23.5), indicative of the expected amplicon length for the nearly complete 16S rRNA gene sequence. When comparing alpha diversity parameters, plastic microbiome samples exhibited higher richness (p-value = 0.028) and biodiversity (p-value = 0.046) values compared to glass samples but lower than water samples (p-value = 1.931E-5 and 2.353E-5, respectively) (Fig 1A). No significant differences in alpha diversity parameters were observed among samples of different newly-introduced plastic polymers (S5 Fig in S1 File) or between them and the debris samples. Moreover, no significant differences were noted in alpha diversity indexes among different time points (all surfaces combined). However, the variance in Shannon index values was smaller at the two-day and 90-day time points compared to the 7, 14, and 30-day time points (Fig 1B, right).

Dissimilarities in the taxonomic composition of the microbiome among samples (beta diversity) were assessed through PCA analysis (Fig 1C). While the samples partially clustered according to time points, there was no clear clustering based on surface type. Principal component 1 (PC1, X-axis), explaining 41.08% of dissimilarities, highlighted the highest inter-sample diversity between the 2- and 90-day samples, with the 7, 14, and 30-day samples clustering together. Notably, the 2-day samples exhibited significantly higher dissimilarities among themselves compared to all other time points, indicating a more pronounced influence of surface type in the early stages of biofilm development.

Plastic-attached microbiome becomes less specific with time

A diverse assortment of plastic items made of variable polymers (S2 Fig in S1 File) was introduced into the experiment to capture a wide range of plastic-associated bacteria for inoculating the new surfaces. This mixture comprised fragments or whole items like cups, bags, straws, plastic cutlery, bottles, and various other mostly fragmented plastic items.

To assess the migration of bacteria to the newly introduced surfaces, we compared the total number of mapped species from the sampled debris of the primary experiment (D1-D4) with that of all plastic surfaces at each time point. Results from the primary experiment revealed that 74.5% of the mapped species were shared between the debris and the plastic surfaces as early as two days after the experiment commenced, indicating rapid bacterial migration from the debris to the surfaces. This proportion remained consistent within the range of 68.4–78.1% throughout the experiment until 90 days (Fig 2A). It’s noteworthy that these numbers may be underestimated as not all debris items were sampled.

Conversely, the proportions of shared mapped species between the glass surfaces and the debris were significantly lower (Fig 2B). Furthermore, comparison of mapped species lists between plastics and glass demonstrated a consistent trend of increasing proportions of shared species between the two surface types (Fig 2C), suggesting a gradual loss of microbiome surface-specificity.

Bacterial relative abundance on different plastic polymers is similar at different temperatures

Identifying dominant taxa within a sample relies on their high read proportions relative to the total read count. In this study, we analyzed the top six most abundant genera within the pooled microbiome of each polymer, integrating data from all time points for that specific polymer (Fig 3). The average seawater temperature along the Mediterranean Israeli coast in the summer months (June-August) is ~28.5°C (https://www.israelweather.co.il/). However, the temperature in tidal pools, that often contain high concentrations of plastic debris, may reach as high as 40°C during the summer [30]. Accordingly, we performed the primary experiment at 28 ± 2°C and the second experiment in extreme 35 ± 2°C. Remarkably, despite the big temperature difference between the primary and the second experiments (7°C on average), nine out of sixteen identified dominant genera were shared between the two experiments and exhibited similar abundance dynamics in the two experiments. The shared genera included Alcanivorax, Alteromonas, Ketobacter, Pseudomonas, Porticoccus, Marinobacter, Sneathiella, Microbulbifer and Spongiobacter. In both experiments, Alcanivorax, Ketobacter, Pseudomonas, Porticoccus, Sneathiella, Microbulbifer, and Spongiobacter were more abundant in PE compared to PET, glass and wood between 7- and 30-days’ time points and Alteromonas seems to be an early generalist colonizer in both experiments.

Fig 3 — (A) Primary experiment at 28±2 °C. (B) Second experiment at 35±2 °C. The top 6 genera of each of the plastic polymer types are represented.

Identification of pivotal genera within succession modules and genera with polymer-specific preference using WGCNA

We employed WGCNA to discern correlations among bacteria based on their relative abundances. This method facilitated the creation of a signed co-occurrence network for our primary experiment. Hierarchical clustering of mapped bacterial species was then conducted, utilizing their read counts across various surfaces and time points (Fig 4A). Subsequently, module eigengenes were generated to capture clusters of bacterial species. To streamline the analysis, we performed a merged dynamic analysis (Fig 4B), resulting in 26 modules (indicated by distinct colors, Fig 4C). Module sizes ranged from 36 species (dark orange) to 541 species (Turquoise). A module-trait relationship table was constructed based on the module eigengenes. Notably, most module eigengenes exhibited positive correlations, often significant, to a single polymer and a specific time point. For instance, the Green Yellow module (Fig 4C, top row) displayed a positive correlation with glass and the 30-day time point (cor = 0.5, p-value = 0.02 and cor = 0.37, p-value = 0.09, respectively), while manifesting negative correlations with all other traits.

Fig 4 — (A) Cluster dendrogram based on the relative abundance patterns of identified bacterial species. Hierarchical clustering was performed using topology overlap measure (TOM). The colors represent the module assignments of the clusters. (B) Merged dynamic module clustering of similar eigengenes based on a merging threshold height = 0.25. (C) Module eigengenes table with module-trait corresponding correlation and significance values.

To identify pivotal genera within specific modules, we focused on species with a strong correlation to the module eigengenes (known as module membership) and associated traits (surface type or time). A genus was considered significant within a module if it comprised at least three species with a module membership score exceeding 0.9. For instance, Alteromonas emerged as a pivotal genus in the Salmon module, harboring 11 mapped species with a correlation exceeding 0.9 to the module (Fig 5A). Although this genus wasn’t specifically associated with a particular surface, it displayed a significant correlation with the 2-day time point. Notably, most Alteromonas species within this module exhibited a negative correlation with glass and a positive correlation with all tested plastic surfaces (Fig 5A on the right), suggesting their role as early colonizers of various plastic polymers. Another example is the genus Dokdonia, which featured six mapped species within the brown module and demonstrated a robust positive correlation with the PS surface and the 2-day time point (Fig 5B), implying their early colonization on PS. Conversely, the genus Nevskia, encompassing six mapped species within the Turquoise module, exhibited a strong positive correlation with PP and the 30-day time point (Fig 5C), indicating a late colonization pattern on this polymer.

Fig 5 — Module membership values of the key genera–*Alteromonas* (A), *Dokdonia* (B) and *Nevskia* (C) are represented in the context of their associate modules: Salmon, Brown and Turquoise accordingly (left side). The corresponding gene significance (GS) charts depicting the level of correlation of the key genera to all traits (surface type and time point) are presented on the right side. Dashed line indicates significance threshold (p-value = 0.05) to trait.

Several genera have been identified as pivotal across multiple modules. For example, the genus Pseudomonas exhibited pivotal roles in five distinct modules, notably prevalent in the green module (with 30 instances, 10 of which had module membership > 0.9, Fig 6A) and the turquoise module (comprising 43 mapped species, 9 with module membership > 0.9, Fig 6B). Pseudomonas in these two modules demonstrated a strong correlation with PE and the 2-day time point (Fig 6C), or with PP and the 30-day time point (Fig 6D), indicating the presence of multiple succession subgroups within the genus.

Fig 6 — (A, B) Distribution of mapped species in modules Green (A) and Turquoise (B) by module membership and significance to the relevant trait (according to module eigengenes). *Pseudomonas* species are emphasized in Red. (C, D) The corresponding gene significance (GS) heat maps for *Pseudomonas*, depicting the level of correlation to each trait (surface types and time points).

We identified 77 module-pivotal genera distributed across 21 modules (Table 1), with 17 modules demonstrating positive correlations with specific surface types and time points. Interestingly, glass-related pivotal genera within the green-yellow and blue modules were predominantly correlated with later succession stages (at 30-day and mainly 90-day time points), suggesting their absence during the early stages (Table 1).

Table 1. Pivotal genera by modules^*.

Surface	Module	Genera	Time-related
PE	Green	Acinetobacter, Halomonas, Luteimonas, Marinobacter, Microbulbifer, Pseudomonas, Vibrio.	2d
	Cyan	Marinomonas, Ochrobactrum, Pseudomonas.	14d
	Light yellow	Pseudomonas.	14d, 30d
PP	Pink	Aquimarina, Cystobacter, Methylobacterium, Nonlabens, Photobacterium, Vibrio.	2d
	Turquoise	Acinetobacter, Aeromicrobium, Alcanivorax, Arenimonas, Bartonella, Desulfovibrio, Geobacter, Halomonas, Lysobacter, Nevskia, Nocardioides, Novosphingobium, Pseudomonas, Salinisphaera, Streptococcus, Streptomyces, Vibrio.	30d
	Tan	Streptomyces.	90d
PS	Brown	Dokdonia, Alteromonas, Aquimarina, Bradyrhizobium, Janthinobacterium, Kocuria, Methylobacterium, Methylorubrum, Microbacterium, Mycetocola, Photobacterium, Pseudoalteromonas, Staphylococcus, Thalassotalea, Vibrio, Winogradskyella.	2d
	Royal blue	Burkholderia, Paraburkholderia.	7d
	Magenta	Aquimarina, Flavobacterium, Streptomyces	14d
	Yellow	Desulfovibrio, Rickettsia, Shinella, Streptomyces	30d
	Light green	Streptomyces.	90d
PET	Red	Celeribacter, Halomonas, Rhodovulum, Saccharospirillum, Shewanella, Simiduia, Vibrio, Winogradskyella.	2d
	Midnight blue	Legionella, Staphylococcus.	14d
	Dark orange	Paenibacillus.	30d
	Black	Mesorhizobium, Paenibacillus, Pontibacter, Streptomyces.	90d
Glass	Green- yellow	Acidovorax, Cupriavidus, Streptomyces.	30d
Glass	Blue	Achromobacter, Amylibacter, Aquabacterium, Chitinophaga, Clostridium, Dechloromonas, Exiguobacterium, Hydrogenophaga, Lewinella, Lysobacter, Massilia, Mesobacillus, Microbacterium, Planococcus, Propionispiram, Propionivibrio, Pseudomonas, Rhizobium, Shewanella, Sphingomonasm, Yoonia.	90d
Surface- unspecific	Salmon	Alteromonas, Leisingera, Paraglaciecola, Phaeobacter, Psychrosphaera, Rhodovulum, Vibrio.	2d
	Grey 60	Marinobacter.	2d
	Light cyan	Gloeocapsopsis.	90d
	Dark turquoise	Achromobacter, Chitinophaga.	90d

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* Genera with at least three species with module membership score > 0.9.

To pinpoint genera enriched on specific plastic polymers, we conducted a screening process focusing on species demonstrating significant correlations with surface type traits compared to other surfaces within the initial month of the experiment (encompassing 2-day, 7-day, 14-day, and 30-day time points). We specifically targeted genera with a p-value ≤ 0.05 for surface significance (according to WGCNA results) and a threshold relative abundance exceeding 0.1%. Utilizing this filtration threshold, we identified 39 genera potentially enriched on specific plastic polymers, with 13 genera associated with PE, 11 with PP, 9 with PS, and 6 with PET (Table 2).

Table 2. Significantly enriched genera on plastic polymers^*.

PE	PP	PS	PET
Aestuariispira (1) Alcanivorax (2) Amphritea (1) Bdellovibrio (1) Ketobacter (1) Marinicella (1) Marinobacterium (2) Microbulbifer (3) Peredibacter (1) Porticoccus (1) Pseudomonas (6) Spongiibacter (1) Umboniibacter (1)	Actinomarinicola (1) Alcanivorax (5) Geobacter (2) Halioglobus (2) Marinobacterium (1) Nevskia (2) Pseudomonas (2) Salinisphaera (3) Sandaracinus (1) Sneathiella (1) Tepidicaulis (1)	Granulosicoccus (2) Kangiella (1) Marinobacter (1) Oceanibaculum (1) Paraburkholderia (2) Pelomonas (2) Pseudomonas (1) Rickettsia (1) Thermogutta (1)	Catenovulum (1) Desulfomicrobium (1) Kordiimonas (1) Methyloversatilis (1) Pelagicoccus (1) Ruegeria (3)

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* Genera with gene significant p-value ≤ 0.05 and average relative abundance ≥ 0.1% within a 30-days immersion period. The number of associated species is indicated in the parenthesis (the full list is in S3 Table in S1 File).

The co-occurrence network shows the clustering of genera according to their polymer preferences

To deepen our comprehension of the relationships among various bacteria on plastic surfaces, we investigated the intra-genus connections within the co-occurrence network (Fig 7). The network was based on minimum threshold values for genus abundance, diversity among samples, and connection strength, as mentioned in the methods section.

Fig 7 — Only genera that passed the abundance filter are represented (see Methods section for details). The relative abundances of the represented genera on the different plastic polymers are shown in the pie charts in different colors: Green—PE, purple—PP, PET—orange and PS—pink. The pie chart sizes represent the general prevalence of each genus. Four identified clusters are marked with Roman letters (I-IV).

The network visualization revealed four partially segregated clusters, with the major cluster labelled as "I" encompassing most of the genera, while the three smaller clusters (II, III, IV) contained the remainder. Cluster I displayed a mix of genera with polymer-based abundance signatures, while clusters II, III, and IV appeared to harbour genera with more unified signatures. Cluster III featured seven genera significantly enriched on polyolefins (PE and PP), with Alcanivorax leading the cluster, followed by Ketobacter and Sneathiella. In contrast, Cluster II comprised four genera exhibiting relatively higher abundances on PS and PET, followed by PP, with Paraburkholderia emerging as the most prevalent genus within this cluster. Cluster IV housed four cyanobacteria genera with elevated abundance on PS and PET, followed by PE. The predominant genus within this cluster was Cyanomargarita.

Discussion

Plastic debris found in the ocean comprises diverse combinations of polymers and additives, each endowed with unique chemical and physical properties. Despite their prevalence, little is known about how these properties influence the colonization and succession of microbial species. In this study, we investigated the successive development of bacterial microbiomes on four of the most abundant plastic polymer pollutants in marine environments: PE, PP, PS, and PET, along with two non-plastic abundant materials—glass and wood. The plastic polymers chosen exhibit distinct chemical structures; PE, PP, and PS feature a C-C backbone with varying side groups (hydrogen, methyl, and phenyl ring, respectively), whereas PET is a copolymer of ethylene glycol and terephthalic acid with an ester group in its backbone. These chemical disparities confer unique properties upon each polymer, including density, hydrophobicity, roughness, surface energy, and chemical reactivity, which collectively influence the adherence and growth of marine microorganisms.

It was shown that as the plastic biofilm cover develops, the effect of the polymer type on the microbiome composition is weakening until it is almost unnoticeable [6, 24]. This notion was validated in a recent study that showed that the polymer type affects microbiome composition and metabolic functionality in early marine plastic communities but not in mature communities [37]. Using linear regression analysis, we recently identified prokaryotic and eukaryotic taxa that were significantly enriched on specific plastic polymers in the marine environment [27].

Our findings show that greater variability is anticipated during the earlier developmental stages of plastic communities. Specifically, our results indicate that the various polymer samples exhibited the highest dissimilarity at the 2-day time point, followed by a gradual, consistent shift toward equilibrium. This observation is further supported by the continual increase in the proportion of shared species between the plastic and glass surfaces over time.

To gain insights into the development of the plastisphere bacterial microbiome across different plastic and non-plastic surfaces, we initially screened our long-read 16S rRNA metabarcoding results for dominant genera based on their relative abundance on specific surface types and over time. While this analysis provided valuable data on highly abundant genera colonizing the surfaces, it lacked statistical significance validation. Upon comparing the lists of the prominent genera from the primary experiment with those from the second experiment, despite differences in temperature ranges (averaging 7°C), debris content from the environment, and a smaller variety of polymers and the shorter time range in the second experiment, we observed that the majority of top-abundant genera (9 out of 16) in the second experiment were shared with those of the primary experiment. This suggests that polymer-based preference mechanisms are highly robust and less influenced by environmental factors.

Next, we utilized WGCNA to analyze the co-occurrence network of bacterial species within the microbiome of the different surfaces. WGCNA has been previously coopted for similar purposes, demonstrating its utility in deciphering complex interactions and associations within microbial communities [38–40].

The implementation of WGCNA in this study allowed us to delineate, for the first time, the succession signatures of plastisphere bacteria at both the genus and species levels, shedding light on their relationships with surface type and time-point dynamics. The validity of our WGCNA results was highlighted by the observation that multiple bacteria mapped to a specific genus were consistently clustered within specific modules, displaying high correlations with their respective module eigengenes in most instances. The findings of this study are reinforced by earlier research. For example, in concurrence with our results, previous studies have identified the genus Alteromonas as comprising pioneer generalist colonizer species [24, 41, 42]. Moreover, several bacterial genera, such as Marinobacter and Marinobacterium, which exhibited significant enrichment on plastic polymers in this study, are recognized as hydrocarbon metabolizers [43, 44]. Furthermore, at least three of the genera that were significantly enriched on PE, according to our analysis, were previously shown to degrade PE, including Alcanivorax [45], Microbulbifer [46] and Pseudomonas [47].

Using the MicrobiomeAnalyst platform, we identified four co-occurrence clusters of dominant bacteria, with three clusters containing genera exhibiting similar polymer-based occurrence signatures. The strong connections among genera within each cluster suggest tight collaborative interactions. We would like to note that while bacterial co-occurrence networks were achieved in this study, inter-kingdom taxa co-occurrence (i.e. between fungi and bacteria) are more challenging to decipher based on DNA metabarcoding analyses alone. This challenge is due to the differences in barcode regions for the different taxonomic groups. However, such interactions were previously reported within the plastisphere (i.e. [48]) and should be further explored.

We have shown in previous studies that using long, full-length 16S rRNA barcode sequences obtained by the nanopore MinION platform enables high-resolution taxonomic profiles of the plastisphere microbiome [49, 50]. The simple experimental design outlined in this study, coupled with the utilization of the nanopore-based metabarcoding pipeline and the analysis tools employed herein, holds practical applicability across various fields of microbiome research. For instance, it could be employed in the biomedical field to elucidate the colonization and succession patterns of pathogenic bacteria on artificial implants. Further fine-tuning of the analysis parameters will be necessary to address such future research inquiries. Nonetheless, we envision significant potential in the research approach presented here to gain deeper insights into microbial succession processes and preferences for specific surfaces or time frames.

Conclusion

This study provides a comprehensive analysis of microbial colonization and succession on various plastic polymers in marine environments, revealing significant patterns in microbial diversity and community dynamics. Despite differences in polymer composition and environmental conditions, the findings highlight rapid bacterial migration to plastic surfaces, with over 74% of the microbial species shared between debris and newly introduced plastics within just two days. This rapid colonization underscores the adaptability of marine microbial communities to this new surface type and their potential role in shaping the plastisphere.

The analysis of diversity parameters revealed that plastic surfaces support unique microbiomes with higher richness and biodiversity than non-plastic surfaces such as glass, but lower than surrounding water samples. Temporal trends indicated that early colonization stages exhibited the most pronounced microbial variability, gradually stabilizing over time. This observation, coupled with the identification of polymer-specific genera such as Alcanivorax and Pseudomonas, demonstrates the interplay between microbial communities and the chemical properties of plastic polymers.

By employing advanced network analysis methods such as WGCNA, this study identified pivotal bacterial genera and their succession dynamics, offering critical insights into the microbial interactions within the plastisphere. These findings contribute to our understanding of microbial ecology in plastic-polluted ecosystems and to developing targeted bioremediation strategies to mitigate the global plastic pollution crisis.

Supporting information

S1 File

(DOCX)

pone.0318843.s001.docx^{(3MB, docx)}

Acknowledgments

The authors are grateful to Prof. Shiri Navon-Venezia for her scientific advice.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This work was supported by the Israel Ministry of Science and Technology Israel-Portugal collaboration Grant 3-1650 and the Israel Science Foundation (ISF) personal Grant 1556/23. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0318843.r001

Decision Letter 0

Arga Chandrashekar Anil

25 Nov 2024

PONE-D-24-38682Unveiling microbial succession dynamics on different plastic surfaces using WGCNAPLOS ONE

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Reviewer #1: General comment

Specific comments:

• Line 75 – spelling mistake – “litter”

• Why are these temperatures (28 and 35 degrees) chosen? Presumably these are water temperatures, although this should be stated, as well as how this was tracked throughout the experiment. Why so warm? What marine conditions are trying to be emulated? I am guessing conditions of the Herzliya coastal waters are trying to be emulated, since this is where the debris was apparently collected, but 35 degrees seems to be warmer that the water ever gets there (according to seatemperature.net). So why was this chosen? The experimental setup needs to be rationalised.

• Why use inoculated seawater as opposed to regular seawater? What were the conditions of the Herzliya marina at time of sampling? Similar water temperature and salinity to setup? What was the sampling date? How were the debris collected? How were they maintained between collection and inoculation to reduce microbial changes?

• How was all the equipment sterilized before use? Otherwise, what is the point of this inoculation approach if the organisms present in the setup are not of plastic origin?

• Were the plastic/wood/glass beads sterile before adding to the aquarium? If so how was this done. If not, how might the implicate the results?

• Why no PP or PS in the higher temperature situation? What are the sizes of the beads.

• Manufacturers of materials needs to be added in all cases, for setup equipment, plastics used, mesh bags etc.

• What chemical composition were the mesh bags? Were they plastic as well (organza can be a wide variety of materials, not scientifically accurate enough). What was the mesh size?

• Was post-sampling processing conducted in a sterile manner? There is no mention of a laminar flow cabinet being used. How was contamination managed?

• Why were wooden beads only subjected to a completely different extraction procedure. Different kits produce different results. Comparability can be problematic and certainly should be discussed.

• Line 155: How were sequences “compared to the NCBI database”. Using BLAST only? What date/what version of the database

• No mention of extraction or PCR blanks in methods. This is highly problematic for publication and must be addressed before the paper can be published.

• Lines 231-236 this information should really be in methods

• Line 256 – why is the temperature delta 7 degrees on average? I thought this was accurately controlled and kept consistent (although it is not explained how this was maintained and monitored, which again is problematic)

• Why are the modules named after colours? It reads as slightly ridiculous. Why can’t they have alpha/numeric identifiers?

• Line 320 (and Table 2 generally): Relative abundance greater than just 0.1% doesn’t sound like enrichment. Why such a low cutoff?

• Table 2: Regarding the number in brackets, number of associated species generally? i.e. 6 of the total number of Pseudomonas species were specific to PE? Or is 6 species all the Pseudomonas species found in the study? It would be important to show average relative abundance of these taxa to show the reader if they really are enriched or simply being detected by chance here and not on other surfaces. What is the P-value based off? What statistics was done? Indicator analysis? If so explain, if not, what analysis was used and why?

• Why use long read as opposed to something shorter but covering more sequence depth, like V3-V4 with Illumina, which is more commonly used. This difference, including what can be better seen with long reads vs shorter barcoding methods should be discussed! It is one of the main differentiating factor compared to other works and should be discussed in depth

• Line 388-390 – Similarly, since WGCNA is the also a major thing that is new/interesting about the study, discussing previous uses of this and how this newer approach enables new insights is also very important to be discussed. Needs to be expanded significantly

• I would expect a conclusion section, but this is absent

Reviewer #2: Thank you for sharing your manuscript. I found the study valuable in advancing our understanding of microbial succession on different plastic polymers in marine environments. The identification of polymer-specific microbial communities and the successional trends across environments are particularly compelling. However, I believe the manuscript would benefit from some adjustments and additions that explain the significance in greater depth.

In particular, your introduction and discussion sections are quite short and lack depth. Contextualizing the broader implications of your findings and expanding on how they contribute to addressing the global plastic pollution crisis would be beneficial. Below are some suggestions for improvement:

Broader Implications:

- Global Context: While the results are well-documented, the manuscript does not sufficiently situate these findings within the larger context of global plastic pollution. It would strengthen the study to articulate how understanding microbial succession on plastics could influence degradation predictions or provide insight into the long-term ecological impacts of persistent plastic waste.

- Ecosystem Effects: The potential for microbial colonization to alter nutrient cycling, carbon fluxes, or interactions with higher trophic levels is an area that could be explored further. For example, could the dominance of certain genera, such as Alcanivorax, signal broader ecological consequences, such as shifts in hydrocarbon degradation pathways?

- Environmental and Policy Relevance: Emphasizing the environmental consistency of polymer-specific communities is valuable, but further discussion on its significance would be helpful. Could these findings inform efforts to design more sustainable plastics or predict ecological risks across environments?

- Plastic Degradation Potential: The identification of genera like Pseudomonas and their potential to influence biodegradation pathways presents an opportunity to discuss the dual implications of microbial activity: both as a possible avenue for mitigating plastic pollution and as a vector for environmental risks (e.g., transport of pathogens or invasive species).

Deepening the Interpretation of Results:

- Ecological Roles of Genera: While the study identifies key microbial genera, there is limited discussion on their ecological functions or roles in shaping succession patterns. For instance, why do some genera dominate later stages of colonization? Are they exploiting specific metabolic niches or competitive advantages within the biofilm matrix?

- Successional Patterns: The observed patterns are well-described, but their drivers are less clear. Additional insights into the mechanisms driving succession (e.g., nutrient availability, surface properties of plastics) would enhance the interpretation.

Tying Findings to Broader Significance:

I recommend linking your results explicitly to the broader ecological and societal implications in the discussion and conclusion. For example:

- In the introduction, briefly outline how understanding microbial colonization can contribute to mitigating the impacts of plastic pollution or developing sustainable solutions.

- In the discussion, dedicate a section to synthesizing the ecological significance of these findings and their relevance to global efforts to address plastic pollution.

- In the conclusion, consider a forward-looking statement about how this research could inform future studies or practical applications, such as plastic design or environmental management.

Lastly there are some small edits I suggest you make e.g explainign why you chose the temperatures you used in your incubation experiments. Furthermore, the use of glass and wood as comparative surfaces in your experiment is a sound choice, as these materials are often used as controls in Plastisphere studies. However, to ensure that the experimental design is clearly understood, I recommend explicitly stating that these materials are being used as controls. This would help to clarify the intent behind including them and would better contextualize the significance of the microbial communities found on plastic surfaces in comparison to those on other materials.

By elaborating on these points, your manuscript could move from being a strong descriptive study to one with a larger impact on how we understand and address plastic pollution in marine environments.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2025 Feb 6;20(2):e0318843. doi: 10.1371/journal.pone.0318843.r002

Author response to Decision Letter 0

15 Dec 2024

Dear Dr. Arga Chandrashekar Anil,

Academic Editor, PLOS ONE.

We want to express our gratitude to you and the reviewers for your thorough evaluation and constructive feedback on our manuscript entitled "Unveiling microbial succession dynamics on different plastic surfaces using WGCNA" The insightful comments and suggestions provided have been invaluable in enhancing the quality and clarity of our work. In this document, we have addressed each reviewer's comments in detail . Our responses include explanations of the revisions, justifications for the approaches taken, and additional data or references where necessary. We hope our revisions meet the reviewers' expectations and further strengthen the manuscript.

General comments

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: The MS was re-checked and adjusted to meet PLOS ONE's style requirements.

2. Please include a caption for tables 1, 2 and figure 7.

Response: Captions for tables 1 and 2 added. The caption for Figure 7 was present.

3. In your Methods section, please provide additional information regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the field site access and, if no permits were required, a brief statement explaining why.

Response: No permit was required. We have positioned the samples in places that did not interfere with the normal operation of the Marina.

4. Thank you for stating the following financial disclosure:

[This work was supported by the Israel Ministry of Science and Technology Israel-Portugal collaboration Grant 3-1650 and the Israel Science Foundation (ISF) personal Grant 1556/23.].

Please state what role the funders took in the study. If the funders had no role, please state: ""The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.""

Response: The statement is correct.

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

5. Thank you for stating the following in the Acknowledgments Section of your manuscript:

[The authors are grateful to Prof. Shiri Navon-Venezia for her scientific advice. This work was supported by the Israel Ministry of Science and Technology Israel-Portugal collaboration Grant 3-1650 and the Israel Science Foundation (ISF) personal Grant 1556/23.]

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript Response: removed and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

[This work was supported by the Israel Ministry of Science and Technology Israel-Portugal collaboration Grant 3-1650 and the Israel Science Foundation (ISF) personal Grant 1556/23.].

Response: The statement is correct.

Reviewer #1: General comment

The subject matter is broadly interesting and although the subject matter (differences in microplastic biofilms) has been relatively well studied, there is some novelty in the use of longer whole 16S reads using nanopore, and the use of weighted correlation network analysis is also interesting. However, there are a number of technical details missing which make the paper as it is currently, unpublishable, and the missing experimental details need to be addressed before the paper can be accepted. The most egregious lacking information concerns how sterility of the experimental setup was achieved, and the handling of DNA extraction & PCR blanks. Response: We accepted Reviewer #1's comment and added details about how sterility was kept throughout the MS. With that being said, we would like to mention that the whole setup was built on land with sterile artificial seawater that was made from dry salt, so the chances for contamination of marine bacteria seem very low. Nevertheless, we took extra measures to ensure sterility, as detailed in the revised version. Without more information on these points specifically, the validity of the entire experiment is called into question. Generally, more justification for the statistical tests used and discussion concerning how different approaches might achieve different results is needed, especially concerning the use of WGCNA. Comparisons to other similar studies and their results needs to be conducted much more comprehensive, especially to other studies which have also used WGCNA. Response: We accept this comment and refer to it in the revised version.

Specific comments:

1. Line 75 – spelling mistake – “litter” .

Response: Corrected.

2. Why are these temperatures (28 and 35 degrees) chosen? Presumably these are water temperatures, although this should be stated, as well as how this was tracked throughout the experiment. Why so warm? What marine conditions are trying to be emulated? I am guessing conditions of the Herzliya coastal waters are trying to be emulated since this is where the debris was apparently collected, but 35 degrees seems to be warmer than the water ever gets there (according to seatemperature.net). So why was this chosen? The experimental setup needs to be rationalised.

Response: While the Eastern Mediterranean Sea (EMS) seawater temperatures rich ~30°C in August, the temperature in tidal pools along the coasts rich up to 40°C and even more (reference added). Since many of these pools contain high concentrations of plastic debris, we expect that plastic-attached bacteria will be exposed to those conditions as well. A section was added (Lines 80-89).

3. Why use inoculated seawater as opposed to regular seawater? Response: Using artificial seawater allowed us to maintain sterility, adjust salinity parameters and make sure there are no contaminations that may temporarily exist in natural seawater. What were the conditions of the Herzliya marina at time of sampling? Response: Details were added (Line 116-117). Similar water temperature and salinity to setup? Response: Yes. What was the sampling date? Response: July 20th 2021 (added to text, Line 116). How were the debris collected? Response: Using a sterile aquarium net. How were they maintained between collection and inoculation to reduce microbial changes? Response: In cooled Styrofoam boxes until reaching the lab (Added to text, Line 119)

4. How was all the equipment sterilized before use? Otherwise, what is the point of this inoculation approach if the organisms present in the setup are not of plastic origin? Response: All equipment, including the aquarium itself, was cleaned and sterilized either with bleach or ethanol. During the experiment all aquarium parts were covered to prevent dust or liquid droplets from entering. Because the setup was based on artificial sterilized seawater and it was separated from any marine contamination, we are assured that no contamination could influence the results. Moreover, our DNA metabarcoding results showed hits of strictly marine or dual (such as E. coli) bacteria and no terrestrial bacteria.

5. Were the plastic/wood/glass beads sterile before adding to the aquarium? If so how was this done? If not, how might they implicate the results?

Response: Yes we added a sentences (Line 104-105): “All materials were soaked in 70% ethanol for sterilization, followed by washing with sterile distilled-deionized water (DDW).”

6. Why no PP or PS in the higher temperature situation? What are the sizes of the beads.

Response: Due to financial constraints associated with nanopore sequencing, PP and PS samples were not included in the second experiment. From these three, PE is the most abundant floating plastic polymer and it was chosen for this experiment. Bead/pellets sizes ranged from 3–5 mm in diameter for plastic beads, 5mm for glass and 6mm for wood beads, as described in the Methods section (Line 100-104).

7. Manufacturers of materials need to be added in all cases, for setup equipment, plastics used, mesh bags, etc.

Response: Manufacturer details have been added to the methods section for all materials and equipment used (Line 100-104).

8. What chemical composition were the mesh bags? Were they plastic as well (organza can be a wide variety of materials, not scientifically accurate enough). What was the mesh size?

Response: The mesh bags were made of polyester with a mesh size of approximately 0.2–0.4 mm. This information has been clarified in the methods section (Line 98-100).

9. Was post-sampling processing conducted in a sterile manner? There is no mention of a laminar flow cabinet being used. How was contamination managed?

Response: Post-sampling processes were conducted under sterile conditions in a laminar flow cabinet. Line 135:”All procedures were conducted inside a laminar hood” Plastic debris and mesh bags were washed with sterile artificial seawater (ASW) to remove unattached materials. Sterile equipment was used throughout.

10. Why were wooden beads only subjected to a completely different extraction procedure. Different kits produce different results. Comparability can be problematic and certainly should be discussed.

Response: DNA extraction from wood using the phenol-chloroform method resulted in low yield and poor quality. Therefore, a commercial DNA extraction kit yielded higher concentration and better purification. Due to these differences, wood samples were excluded from most analyses and are only presented in Figure 3.

11. Line 155: How were sequences “compared to the NCBI database”. Using BLAST only? What date/what version of the database

Response: The 16S rRNA sequences were compared to the NCBI bacterial 16S database using the EPI2ME 16S workflow provided by Oxford Nanopore Technologies. This workflow utilizes a proprietary algorithm for taxonomic assignment. The specific version of the workflow used was v2023.04.21-1804452, accessed via the EPI2ME desktop agent. The database used by the workflow corresponds to the NCBI bacterial 16S database.

12. No mention of extraction or PCR blanks in methods. This is highly problematic for publication and must be addressed before the paper can be published.

Response: PCR blanks are conducted in all our PCR routines to test for primer and foreign DNA contaminations. We don’t think this obvious control should be mentioned in the MS.

13. Lines 231-236 this information should really be in methods

Response: Accepted. This information has been moved to the methods section (line 204-216)

14. Line 256 – why is the temperature delta 7 degrees on average? I thought this was accurately controlled and kept consistent (although it is not explained how this was maintained and monitored, which again is problematic)

Response: This line refers to Figure 3, which illustrates the temperature differences between the primary experiment (28°C) and the secondary experiment (35°C) as mentioned in the text. We made modifications to clarify it in the text.

15. Why are the modules named after colours? It reads as slightly ridiculous. Why can’t they have alpha/numeric identifiers?

Response: WGCNA (Weighted Gene Co-expression Network Analysis) assigns modules color names as a default convention to facilitate easy distinction. For reference, see Langfelder and Horvath (2008): DOI: 10.1186/1471-2105-9-559.

16. Line 320 (and Table 2 generally): Relative abundance greater than just 0.1% doesn’t sound like enrichment. Why such a low cutoff?

Response: The cutoff of 0.1% was just the threshold cutoff of the data that was used for the WGCNA analysis to prevent errors related to random contaminations. We added the word “threshold” to clarify this.

17. Table 2: Regarding the number in brackets, number of associated species generally? i.e. 6 of the total number of Pseudomonas species were specific to PE? Or is 6 species all the Pseudomonas species found in the study? It would be important to show average relative abundance of these taxa to show the reader if they really are enriched or simply being detected by chance here and not on other surfaces. What is the P-value based off? What statistics was done? Indicator analysis? If so explain, if not, what analysis was used and why?

Response: The numbers in brackets indicate the species specifically associated with each polymer type based on WGCNA results. For example, 6 Pseudomonas species (out of ~180 in the study) were associated with PE. Table 2 and Supplementary Table S3 were generated using WGCNA results, combined with relative abundance data,

We have updated Supplementary S3 Table to include relative abundance values for clarity. The P-values are derived from WGCNA correlations.

18. Why use long read as opposed to something shorter but covering more sequence depth, like V3-V4 with Illumina, which is more commonly used. This difference, including what can be better seen with long reads vs shorter barcoding methods should be discussed! It is one of the main differentiating factor compared to other works and should be discussed in depth

Response: We have added a secession in the discussion mentioning the advantage of nanopore full 16s sequencing (Lines 484-489)

19. Line 388-390 – Similarly, since WGCNA is the also a major thing that is new/interesting about the study, discussing previous uses of this and how this newer approach enables new insights is also very important to be discussed. Needs to be expanded significantly

Response: The advantages and novelty about the use of the technique in this study is described in the discussion (Lines 456-459)

20. I would expect a conclusion section, but this is absent

A conclusion section was added.

Reviewer #2:

Thank you for sharing your manuscript. I found the study valuable in advancing our understanding of microbial succession on different plastic polymers in marine environments. The identification of polymer-specific microbial communities and the successional trends across environments are particularly compelling. However, I believe the manuscript would benefit from some adjustments and additions that explain the significance in greater depth.

Response: We thank the reviewer for this comment and expended our introduction and discussion sections accordingly.

Broader Implications:

- Plastic Degradation Potential: The identification of ge

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PLoS One. doi: 10.1371/journal.pone.0318843.r003

Decision Letter 1

Arga Chandrashekar Anil

19 Jan 2025

PONE-D-24-38682R1Unveiling microbial succession dynamics on different plastic surfaces using WGCNAPLOS ONE

Dear Dr. Oren,

I have one minor point to be made regarding point 12 of the comments offered by reviewer 1: the authors can include a statement to manuscript and not simply brush aside the obvious.

Please submit your revised manuscript by Mar 05 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Arga Chandrashekar Anil, Ph. D., D. Agr.,

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

I have one minor point to be made regarding point 12 of the comments offered by reviewer 1: the authors can include a statement to manuscript and not simply brush aside the obvious.

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PLoS One. 2025 Feb 6;20(2):e0318843. doi: 10.1371/journal.pone.0318843.r004

Author response to Decision Letter 1

21 Jan 2025

Dear Dr. Arga Chandrashekar Anil,

Academic Editor, PLOS ONE.

We thank you for providing us with the opportunity to revise our manuscript, entitled "Unveiling microbial succession dynamics on different plastic surfaces using WGCNA" We are grateful for the feedback and have carefully addressed all points raised during this second round of review. Below, we provide detailed responses to the comments and outline the corresponding revisions made to the manuscript.

Editor Comments:

1. I have one minor point to be made regarding point 12 of the comments offered by reviewer 1: the authors can include a statement to manuscript and not simply brush aside the obvious.

Response: This comment refers to reviewer 1 comments: "No mention of extraction or PCR blanks in methods. This is highly problematic for publication and must be addressed before the paper can be published."

In our initial response, we explained that PCR blanks are routinely used in all our PCR workflows to test for primer and foreign DNA contamination and indicated that we did not think it was necessary to explicitly state this in the manuscript. However, we now understand the importance of providing this information explicitly to ensure transparency and to align with the journal’s expectations. Accordingly, we have revised the Methods section to explicitly mention the inclusion of PCR blanks. The updated text now states: “All PCR procedures include PCR blanks to detect potential foreign DNA contamination in the water, reagents or primers" (line 166). We believe this addition addresses the concern and provides the necessary detail to ensure clarity for readers.

Journal Requirements:

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Response: We have thoroughly reviewed the reference list to ensure that all citations are accurate and complete.

We trust that these revisions and responses address all concerns raised by the reviewers and the editorial team. We appreciate your continued guidance throughout this process and are happy to address any further questions or requirements.

Thank you for your time and consideration.

For the Authors,

Keren Davidov & Matan Oren

Attachment

Submitted filename: Response to Reviewers plosone final_2.docx

pone.0318843.s003.docx^{(23KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0318843.r005

Decision Letter 2

Arga Chandrashekar Anil

23 Jan 2025

Unveiling microbial succession dynamics on different plastic surfaces using WGCNA

PONE-D-24-38682R2

Dear Dr. Oren,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Arga Chandrashekar Anil, Ph. D., D. Agr.,

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0318843.r006

Acceptance letter

Arga Chandrashekar Anil

28 Jan 2025

PONE-D-24-38682R2

PLOS ONE

Dear Dr. Oren,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File

(DOCX)

pone.0318843.s001.docx^{(3MB, docx)}

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Submitted filename: Response to Reviewers plosone final.docx

pone.0318843.s002.docx^{(33.5KB, docx)}

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Submitted filename: Response to Reviewers plosone final_2.docx

pone.0318843.s003.docx^{(23KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

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PERMALINK

Unveiling microbial succession dynamics on different plastic surfaces using WGCNA

Keren Davidov

Sheli Itzahri

Liat Anabel Sinberger

Matan Oren

Roles

Abstract

Introduction

Material and methods

Experiments setup

Inoculation and sampling

DNA extraction

Fourier-transform infrared spectroscopy (FTIR)

MinION library preparation and multiplexed nanopore sequencing

Data analysis

Diversity analysis

WGCNA analysis

Co-occurrence network visualization

Results

Metabarcoding outcomes and microbiome diversity

Fig 1. Diversity parameters.

Plastic-attached microbiome becomes less specific with time

Fig 2. Shared and unique mapped species between debris, plastic (all polymers combined), and glass.

Bacterial relative abundance on different plastic polymers is similar at different temperatures

Fig 3. Relative abundance of top abundant genera.

Identification of pivotal genera within succession modules and genera with polymer-specific preference using WGCNA

Fig 4. Cluster dendrograms and module assignments.

Fig 5. Examples of pivotal genera within modules.

Fig 6. Pseudomonas genus demonstrates succession sub-groups.

Table 1. Pivotal genera by modules*.

Table 2. Significantly enriched genera on plastic polymers*.

The co-occurrence network shows the clustering of genera according to their polymer preferences

Fig 7. Co-occurrence network of dominant plastisphere genera.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Arga Chandrashekar Anil

Roles

Author response to Decision Letter 0

Decision Letter 1

Arga Chandrashekar Anil

Roles

Author response to Decision Letter 1

Decision Letter 2

Arga Chandrashekar Anil

Roles

Acceptance letter

Arga Chandrashekar Anil

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Pivotal genera by modules^*.

Table 2. Significantly enriched genera on plastic polymers^*.