Seed Molecule-Guided Strategy Facilitates the Precise Identification of "Hidden" Polymer Breakdown Products

doi:10.21203/rs.3.rs-3997133/v1

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Seed Molecule-Guided Strategy Facilitates the Precise Identification of "Hidden" Polymer Breakdown Products

https://doi.org/10.21203/rs.3.rs-3997133/v1

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Microplastics (MPs) are synthetic polymers that cause severe environmental pollution. However, most studies have primarily focused on the polymers themselves and little information is available regarding their breakdown products during environmental and biological processes. Identifying them is challenging due to structural diversity, especially with variations in both degree of polymerization (DP) and end-group modifications. In this study, we developed a non-targeted platform, named "Oligomer-Finder", which utilizes the liquid chromatography-high resolution mass spectrometry (LC-HRMS) for the screening and annotation of oligomers. Oligomer-Finder is based on the characteristics of oligomers, including repeated neutral losses (rNL), relationship between retention time (RT) and DP, and the mass of end groups (EG). The annotation from mass to structure was aided by custom-built polymer oligomer database (PODB) and oligomer end-group databases (OEGDBs). "Oligomer-Finder" identified dozens of unreported polymer oligomers with various end structures modified by nucleophiles in environmental and biological samples. Additionally, it revealed hundreds of mass spectrometry (MS) features representing unknown polymer breakdown products. Using poly (lactic acid) (PLA) as a model biodegradable plastic, the covalent modification of proteins by oligomers was first discovered, suggesting their biological activity. Our results demonstrate that "Oligomer-Finder" with user-friendly interface can effectively pinpoint oligomers and substantially expand unknown polymer breakdown products, allowing a life-cycle risk assessment.

Earth and environmental sciences/Environmental sciences/Environmental chemistry/Environmental monitoring

Physical sciences/Chemistry/Analytical chemistry/Mass spectrometry

Physical sciences/Nanoscience and technology/Nanotoxicology

Synthetic polymers play an important role in daily life, with plastic emerging as a leading global consumer product. By 2019, its annual production increased to approximately 500 million tonnes^1–3. Estimates for global plastic waste emissions into rivers, lakes, and oceans in 2016 ranged from 19 to 23 million tons and are estimated to increase to 90 million tons per year by 2030⁴. During their life cycles, various chemical compounds employed in plastic production have the potential to leach, posing risks to human health and the environment. Of > 10,000 relevant substances, > 2,400 meet the criteria for persistence, bioaccumulation, and toxicity⁵. Of particular concern is polymer fragmentation into micro- and nano-particles, which increases the surface area and consequently facilitates the release of chemicals embedded within the plastic material⁶. These include additives, residual unpolymerized monomers, and oligomers⁷. Oligomers are defined as molecules composed of a limited number of monomeric units. Polymer oligomers, spanning from 2 to 40 repeating units, are classified as non-intentionally added substances (NIAS) within polymer products^{8, 9}. For instance, poly (ethylene naphthalate) (PEN) materials have been reported to contain 0.81% oligomers by weight¹⁰, whereas poly (ethylene terephthalate) (PET) materials may contain up to 1.3% oligomers. Oligomers were found as a major fraction of the released submicrometre particles released during washing of 12 different polyester textiles¹¹. They may arise not only due to incomplete polymerization, but also as a consequence of polymer chain fracture. In addition, environmental factors such as microbes, sunlight, heat, and oxygen can influence the type of oligomers generated through degradation¹². They manifest a wide range of structural diversity, determined by the inherited repeat unit (RU) from their parent polymers, as well as the end group (EG) and degree of polymerization (DP)⁹. Recent studies have underscored the importance of recognizing oligomers as a distinct class of pollutants.One of the most widely used petroleum-based plastics, styrene oligomers, at 5*10^− 4 M, has been found to act as an endocrine disruptor¹³. The widespread adoption of novel polymers has led to an unexpected increasing presence of oligomers. Particularly, biodegradable plastics, often promoted as a solution to mitigate "white pollution," are more prone to release microplastics (MPs) and oligomers^{2, 14}. Ingested poly (lactic acid) (PLA) particles, the most prevalent bioplastic, can generate a significant amount of oligomers through the activity of digestive enzymes, resulting in intestinal inflammation¹⁵. Elevated concentrations of shorter poly (caprolactone) (PCL), specifically 1 µg/mL for freshwater microorganisms and 1 mg/mL for marine algae and mammalian cells, harmed the examined organisms¹⁶.

Nevertheless, the presence of oligomers remains a subject of uncertainty in the realms of polymer production, environmental assessments, and toxicity experiments. Oligomers have often been overlooked by researchers because of their status as degradation intermediates between polymers and final mineralization products. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is commonly employed for the screening of suspected oligomers emanating from food contact materials¹⁷. However, this approach requires the availability of parent polymer characteristics and a predetermined list of potential oligomers reported in the literature, rendering it unsuitable for complex samples with unidentified polymer origins. Furthermore, the absence of corresponding chemical standards makes precise identification of these oligomers challenging. As a result, the type, structure, and origin of the oligomers released from products and MPs into the environment or human body remain largely undisclosed¹⁸. Therefore, thorough identification of oligomers may necessitate supplementary guidelines, corroborative evidence, and the development of automated non-targeted screening protocols.

In this study, we introduced a rapid, accurate, and sensitive non-targeted screening platform known as Oligomer-Finder. For the first time, we propose a new LC-HRMS workflow customized for the detection of polymer chain scission products. This approach focuses on the identification of seed oligomers and the subsequent search for neighbouring oligomers. The effectiveness relies on the unique structural attributes of oligomers and employs mass spectrometer as the primary screening and identification tool. To validate the efficacy of our algorithm, we analysed oligomers in landfill leachate and oligomers leached from five different types of MPs, identifying up to 176 oligomers with a molecular weight (MW) of less than 1,000 Da. Furthermore, we explored the cysteine (Cys)-modified oligomers from four different biodegradable MPs by Oligomer-Finder. Oligo(lactic acid) (OLA) demonstrate reactive capabilities for covalent modification of proteins when exposed to the human cells. Our proposed Oligomer-Finder facilitates rapid screening and annotation of oligomers originating from a diverse range of polymers. Data processing, completed within minutes, enhances confidence and can be applied to analyse polymer degradation products, providing critical information for assessing the fate and toxicity of oligomers.

Seed molecule-guided non-targeted oligomer screening framework.

A particular polymer can release a diverse range of oligomers sharing the same RU¹⁹. They were designated as oligo(RU)_nα,ω, where "n" indicates the DP, and "α,ω" denotes the possible combinations of EGs (α and ω end structures). Based on whether the end structure was the same as that of the parent polymer, the oligomers were further divided into homologues and congeners (Fig. 1a). As shown in Fig. 1b, OLA_nH,OH, a homolog of PLA, constitutes the predominant distributions of OLA. All these homologues are terminated with a hydroxyl group at the end. OLA_nα,ω represents congeners of PLA with various end structures. Different congeners are composed of the same RU ([LA], C₃H₄O₂).

We developed the Oligomer-Finder workflow to identify all oligomers in the samples. This strategy employed a seed-guided approach to expand and identify additional elements. The underlying principle was to first identify seed oligomers exhibiting repeated neutral losses (rNL) in tandem mass spectrometry (MS2) spectrum. The seed oligomer can provide RU and EG information regarding the parent polymer. As shown in Fig. 1c, all the oligomers derived from the same polymer were detected using seed oligomer guidance. Each seed oligomer was assigned a confidence degree ranging from Degree 4 to Degree 1, enabling the assessment of concern levels and prioritization of the related analysis (see Methods). The oligomers from each polymer were identified as the cycle continued until the last seed oligomer. Figure 1d summarizes the steps of Oligomer-Finder. Three fundamental script packages, namely "Seed oligomer-Finder," "Homologue-Finder," and "Congener-Finder," coupled with custom databases, enable comprehensive oligomer annotation through MS data. A graphical user interface (GUI) version of Oligomer-Finder was provided for easy use. The entire process diagram for oligomer screening is shown in Fig. 1e. MS data were obtained using LC-HRMS, and pre-analysis was performed to obtain the MS1 peak table and MS2 spectra. After the peak table and spectra were imported into the software, homologues and congeners were identified by analysing the mass differences between the peaks of the seed oligomer or RUs and all MS1 peaks (details in Supplementary Fig. S1 and Methods).

Oligomers with and without end-group modification share repeated neutral loss in MS fragmentation.

All oligomers exhibited a repeated neutral loss (rNL) pattern in the MS/MS analyses, generating a series of less-polymerized homologous ions within the collision cell (Fig. 2a). In the case of OLA_6H,OH (HO-[LA]₆-H), it exhibited a series of signal peaks according to [OLA_nH,OH -H]⁻ (n ≤ 6) which were associated with rNL of 72.0236 Da (mass of [LA]), corresponding to the RU of PLA shown in Fig. 2b. The spectral pattern of OLA_4H,Meo with a methoxy end structure, was the same as those of OLA_6H,OH in Supplementary Fig. S2. The MS2 patterns of the oligomers from different polymers were similar; a set of normally distributed signals representing low-DP fragments losing RUs was observed in the MS2 spectrum of each oligomer. The polymers, poly (hydroxybutyrate) (PHB) with an rNL of 86.0368 Da, corresponding to RU ([HB], C₄H₆O₂), and PCL with an rNL of 114.0681 Da, corresponding to RU ([CL], C₆H₁₀O₂), readily released a diverse range of oligomers (Supplementary Fig. S3a–c for oligohydroxybutyrates (OHB_nH,OH, and HO-[C₄H₆O₂]_n-H) and Supplementary Fig. S3e–f for the oligocaprolactones (OCL_nH,OH, and HO-[C₆H₁₀O₂]_n-H), respectively).

The number of NL occurrences quantified were designated as "Count," which were always less than DP but higher than 1. Notably, the most frequently occurring rNL had the maximum "Count" value (Fig. 2c). Oligomers possess distinctive structural characteristics and manifest specific patterns in MS2, thus presenting opportunities for non-targeted screening. The presence of rNL and Count serve as the "criteria" for oligomer screening. To identify potential oligomers, a custom R script termed "Seed oligomer-Finder" was developed (details in Supplementary Fig. S1 and Methods). The logic of the algorithm involves computing all NLs through pairwise subtraction of product ions in a single cycle and recording the NL with the highest count, which is represented as rNL.

The detection limit of "Seed oligomer-Finder" was determined by the Count value of rNL. The Count for a dimer was 1, indicating that its rNL cannot be discerned from a multitude of NLs. The theoretical maximum Count increased with increasing DPs. However, the quality of their MS2 spectra also imposed certain requirements, as evidenced by the Count. This was primarily influenced by the normalized collision energy (NCE) of MS2 and the intensity of the oligomers. For instance, OLA_13H,OH exhibited a count of 12 with NCE set at 20%, whereas the count diminished to only 1 at NCE 60% (Supplementary Fig. S4a,b). High collision energies tend to generate fragment ions, primarily comprising monomers and dimers. A higher count signifies a superior spectral quality, which is advantageous for oligomer retrieval. The NCE was adjusted to optimize the rNL Count, as illustrated in Fig. 2d. Specifically, a stepped NCE approach of 10%-20%-30% was employed for the analysis of the oligomers.

The intensity of the MS1 peak of the oligomer significantly influenced the quality of the MS2 spectrum used for oligomer screening. At lower oligomer concentrations, the MS2 spectrum were incomplete. For instance, the spectrum of OLA_5H,OH demonstrated a Count of 4 with an intensity of 1.09E7, whereas the Count decreased to only 1 at 7.58E4 (Supplementary Fig. S4c,d). The OLA solution was obtained from the PLA MP leachate by mimicking the leaching process (see Methods). The reconstituted OLA solution prior to dilution was labelled as "High, " while the solution diluted 5 times is labelled as "Medium," and the solution diluted ten times was labelled as "Low." The OLA_nH,OH Counts at different concentrations are presented in Fig. 2e. Among these, oligomers with low abundance exhibited diminished MS2 spectrum quality at lower concentrations or may have even posed difficulties in obtaining MS2 spectra in data-dependent acquisition (DDA) mode. Hence, as relying solely on "Seed oligomer-Finder" proves insufficient in screening all oligomers, it is imperative to also consider oligomers in MS1.

Predictability of oligomer retention time.

Polymers and oligomers undergo depolymerization and often coexist with numerous homologues. Under our LC experimental conditions, the shorter the retention time (RT), the stronger the hydrophilicity of the substances. The RT is a reflection of chemical hydrophobicity²¹. Long-chain oligomers exhibit strong hydrophobicity²². This indicates that the long chains of the oligomers have a long RT. In the LC-MS analysis of the PLA leachate, OLA homologues were detected, and there was a robust logarithmic correlation between their DPs and LC RT (Fig. 3a and Supplementary Table S1). In the OLA_nH,OH (n = 4–13) chromatograms, there was a notable association between the natural logarithm of DP (ln(DP)) and RT (Fig. 3b). Moreover, the correlation was steady, irrespective of the LC gradient method employed (Fig. 3c). Analogous correlations for broader DP ranges of OHB_nH,OH (n = 2–12) and OCL_nH,OH (n = 2–8) are depicted Supplementary Figure S5 ~ 6.

Seed oligomers, which are often polydispersible, coexist with their homologues, encompassing parent compounds with higher DP or degradation products with lower DP. The feasibility of using the RT of a seed oligomer and its homologue to predict the RT of other homologues with different DP was validated for OLA_nH,OH. The straight line fitted to represent the relationship between RT and the ln(DP), based on any two DPs, generated predictions for other oligomer DPs, all within a deviation range of 0.32 minutes (Fig. 3d). For heightened prediction accuracy, it is imperative to discern the selection of two points that favour a wider DP disparity. This factor was integrated into the RT prediction algorithm in the Homologue Finder. It is anticipated that this retention time prediction method does not rely on specific chromatographic systems or standard substance calibrations.

The predicted RTs were used to correct the screening results of the homologues (see Methods). The false positive rate (FPR) and false negative rate (FNR) of OLA homologues, predicted on the seed oligomer ([OLA_13H,OH -H]⁻, m/z 953.2823, exact m/z 953.2779694), are shown in Fig. 3e, along with the FPR after manual interference exclusion based on the predicted RT. This approach effectively mitigates the FPR induced by excessively large mass error settings and achieves a balance between the FPR and FNR in screening outcomes.

Oligomers with diverse end-group modifications.

To further elucidate the relationship between the oligomers and parent polymer and summarize their structure and MS information, we established a polymer oligomer database (PODB) of 171 pieces of polymer information based on existing polymer databases (Fig. 4a and Supplementary Fig. S7, details in Methods), which were also used for oligomer structure annotation by MS.

For congeners, a customized type of oligomer, the end structures differ from those of the parent polymer, suggesting modifications by other chemicals²³. An oligomer end group database (OEGDB) was established through literature collection and OLA_4α,ω spectra analysis, facilitating congener screening in Oligomer-Finder (Fig. 4b and Supplementary Fig. S8, details in the Methods section). Additional databases of potential oligomeric EGs in the environment (OEGDB-env) ²⁴ and biology (OEGDB-bio) ^{25, 26} were established through a thorough literature review and analysis of additional experimental MS2 spectra for Degree 2 seed oligomer candidates (Fig. 4b and Methods). These included 15 and 30 pieces of EG information, respectively, providing the potential for the further identification of additional modified end structures. OEGDB facilitated the discovery of OLA_nAc,H and its structure was confirmed through the fragment ion annotation of OLA_4Ac,H in Fig. 4e. In the water-leached sample of PLA MPs, 29 OLA congeners encompassing six distinct types of end structures were screened, each displaying a special peak intensity and RT (Fig. 4c and Supplementary Table S2). The MS2 spectra of OLA_4Ac,H confirmed the presence of RU and the structure of EG.

To further evaluate the effect of end-group modifications on the biological effects of these oligomers, we predicted their ADMET properties using ADMETlab 2.0 software, based on congener structure identification by MS/MS²⁷. A significant difference in toxicity to Daphnia magna (D. magna) was observed between OLA_nH,OH and OLA_nα,ω, indicating that the end structures of the oligomers played a crucial role in determining toxicity. Specifically, congeners demonstrated a 48 h Daphnia magna 50 percent lethal concentration (LC₅₀ D. magna) of 1 g/L or lower, which was significantly lower than that of homologues with concentrations higher than 2 g/L (Fig. 4e). For highly associated pathways, both the DPs and end structures of the oligomers were crucial in determining the probability of activating androgen receptor (AR) (Supplementary Fig. S9a). For all congeners (n = 3–6), the probability of activating nuclear peroxisome proliferator-activated receptor gamma (PPAR-γ) exceeded 50% (Supplementary Fig. S9b). Given the widespread use of PLA as a multifilm and compostable bag in agricultural fields, it has the potential to enter rivers after fragmentation. The degradation products may pose chemical disturbances to the ecosystem, exhibiting toxicity to D. magna and serving both as a predator and prey. Furthermore, it has been reported that oligomers with different end groups (OH or Ac group) exhibit varying levels of toxicity, particularly higher than that of PCL MPs²⁸. End-capped oligomers are likely to be more significant than non-modified oligomers, suggesting higher toxicity for congeners than for homologues.

Microplastic breakdown products in environmental samples.

In theory, during the degradation process, polymers inevitably pass through a range of oligomer molecular weights before reaching monomers and complete mineralization¹. The total amount of oligomers would far exceed that of the main additives that have garnered significant attention (ranging from 0.05 to 70 µg/L)²⁹. In this study, we used the proposed Oligomer-Finder approach to screen oligomers in two sample types: MP water leachate and original landfill leachate. Five types of MPs, PLA, PHB, PCL, poly (butylene succinate) (PBS), and nylon6-poly (caprolactam) (PA6), were used in water leaching experiments. We selected a leaching time of 7 d to gain insights into the environmental consequences associated with the oligomer release of MPs into water. Details of sample preparation can be found in the Methods and Supplementary. A holistic workflow for the non-targeted analysis of oligomers is depicted in Supplementary Figure S10a.

The results from the "Seed oligomer-Finder" for the analysis of leachate revealed a rapid surge in the number of molecules with RUs within the first day, followed by a gradual decrease in the detection numbers over the subsequent days (Fig. 5b). With the aid of PODB and OEGDB, a diverse array of oligomers originating from five polymer types, various DPs, and EGs were successfully discovered. By exclusively relying on Degree 1 seed oligomers for screening, the analysis of homologues and congeners significantly broadened the spectrum of detectable oligomers (from 4 to 176) (Fig. 5c). The number of oligomers released from the five polymers rapidly increased in water within a day, followed by a gradual transformation into monomers or aggregation into minuscule particles through degradation out of the detection range. All MP inputs were detected on the third day of sampling, indicating that the ability of MPs to release oligomers depends on their type and morphology.

The landfill leachate revealed various types of seed oligomer candidates with different degrees of confidence, and the two candidates with Degree 1 were from PET (Fig. 5d, Supplementary Fig. S11). PET MPs were detected at an abundance of 1–2 items/g in the leachate³⁰. Further oligomer analysis led to the additional identification of OET_nH,OH (n = 2–5) (shown in Fig. 5e and Supplementary Table S3). OET is considered a NIAS and has been previously demonstrated to migrate from plastic materials into food^{32, 36}. The in silico assessment raised concerns regarding the genotoxicity of OET and their hydrolysis products³¹. Given the extensive utilization of PET in beverage bottles and other food packaging materials, its oligomers could potentially be considered new pollutants.

In vitro degradation of degradable MPs to release oligomers with cysteine-modified end structures.

Modification of the end structures of the screened congeners revealed the involvement of nucleophilic chemicals in oligomer formation. Functional amino acids that play critical roles in catalysis and regulation display elevated nucleophilicity and can be selectively targeted for covalent modifications by reactive electrophiles³². When exposed to MPs in a biological environment, oligomers may conjugate with endogenous small molecules during in vivo release.

The utilization of OEGDB-bio (with Cys included for its sulfhydryl group is nucleophilic) in "Congener-Finder" mode of Oligomer-Finder proves beneficial for the discovery of oligomers modified by metabolites. In this study, incubation with simulated small intestine medium showed that four types of degradable polyesters (PLA, PHB, PCL, and PBS) could conjugate with Cys through end structure modification, releasing a series of oligomers (OLA_nH,Cys, OHB_nH,Cys, OCL_nH,Cys and OBS_nH,Cys, as illustrated in Fig. 6a and detailed in Supplementary Table S4. The MS2 spectrum of OLA_6H,Cys shown in Fig. 6b contains the fragment ${y}_{4}^{-}$ conjectured as [OLA_4H,Cys -H]⁻. Fewer OCL conjugates were detected, suggesting that the PCL MPs exhibited either no reactivity or low reactivity with Cys.

OLA covalently bind to cysteine residues of human proteins.

To determine whether oligomers can modify the cysteine residues of proteins and which proteins may be affected, proteins in liver-derived HepG₂ cells exposed to OLA were profiled using an LC-MS-based shotgun proteome approach (Fig. 6c, details in Methods). HepG₂ cells were selected because the liver is a major organ for the metabolism of exogenous substances, and our previous study found that OLA and their nanoparticles bioaccumulated in the liver¹⁵. Three possible OLA_nH,OH (n = 2–4) modification patterns identified from the above results were added to the cysteine (C) residues in Proteome Discoverer 3.1 as variable modifications. The variable modification settings are shown in Supplementary Table S5. Twenty proteins were identified by oligomer modifications. These were assessed using experimental group-specific criteria, with consideration given to the presence of rNL in the spectra (Supplementary Fig. S13 and Supplementary Table S6). A network consisting of 14 OLA-modified proteins was established in the protein–protein interaction network within STRING³³ (Fig. 6d). Taking VDAC2 as an example, the modified peptide ¹MATHGQTCARPMCIPPSYADLGK²³ produced a molecular ion of [M + 2H]³⁺ at m/z 826.3871, which increased by 144 Da. The fragment ion of ${b}_{12}^{2+}$ was also observed to increase by 144 Da, suggesting that the cysteine at position 8 (Cys8) should be modified by OLA_2H,OH. Moreover, the other fragmentation ions of ${y}_{6}^{+}$ and ${y}_{1}^{+}$~ ${y}_{9}^{+}$had the same mass as the original peptide, indicating that the peptide belonged to MATHGQTCARPMCIPPSYADLGK (Supplementary Fig. S12). In addition, the fragment ion of ${y}_{16}^{+}$ and ${y}_{16}^{2+}$ was observed to increase by 72 Da, which suggested that the peptide modified by OLA_2H,OH would also exhibit the rNL of OLA ([LA] in Fig. 6e). This result further confirms that oligomers can covalently modify the Cys residues of HUMAN_VADC2 and that RU can provide additional information for the quick screening of modified peptides. VADC2 forms a voltage-dependent anion channel through the mitochondrial outer membrane that allows the diffusion of small hydrophilic molecules³⁴. OLA was reported for the first time, providing more possible modification patterns for subsequent identification of the modified protein. Protein adduct formation might give rise to toxicity by disrupting protein structures and/or functions or by provoking an immune response and hypersensitivity, followed by a series of downstream effects²⁵.

The Polymer Database (PoLyInfo), established by the Japan National Institute for Materials Science (NIMS), encompasses more than 25,000 polymers derived from approximately 20,000 monomers³⁵. New polymers, including degradable plastics and polymeric flame retardants (FRs), are undergoing rapid development and are becoming increasingly ubiquitous in our daily lives. A broad range of blended polymers comprising plastics consistently release a diverse spectrum of oligomers into the environment. Several oligomers have been identified, but a large number remain unknown owing to gaps in the analytical methods for homologues and congeners. The application of Oligomer-Finder, a seed molecule-guided screening strategy, significantly increased the number of annotated oligomers. The presence of unknown oligomers in environmental and biological samples has raised concerns. Compared to the suspected screening of oligomers from specific plastics in previous studies, Oligomer-Finder demonstrated notable independence from prior knowledge. Additionally, compared to screening oligomers using conventional non-targeted analysis tools such as MS-Finder, Oligomer-Finder exhibited a distinct advantage in identifying oligomer structures not included in chemical databases.

Notably, the MS/MS coverage in instrument analysis poses another constraint for non-targeted screening. Screening based solely on peaks with MS2 spectra may not encompass all compounds, especially those present at low concentrations. In Oligomer-Finder, all MS1 peaks were bravely taken into account to screen oligomers based on seed oligomer to reduce false negatives and false positives. Thus, we further developed a homologous screening algorithm and predicted the RT to avoid false positives from in-source fragmentation. Congeners are a less-known class of substances with different EGs than the parent polymers. The screening algorithm assumes that an EG exists and matches the mass of EGs listed in the OEGDBs. This seed-molecule-guided strategy ensures the coverage and quality of screening. However, the annotations provided by Oligomer -Finder are limited by the constraints of MS and cannot serve as a substitute for the definitive confirmation offered by chemical standards, thus ensuring an unambiguous annotation. The synthesis challenges associated with achieving specific DP and EG hinder further qualitative and quantitative analyses of the oligomers. Additional oligomers at lower confidence levels were present in the results and were often characterized by rare end-group modifications or an unidentified polymer origin.

It is crucial to consider the contribution of these released oligomers to the toxicity assessment of micro- and nanoplastics³⁶. Most of the identified products were found for the first time and our method has greatly increased the chemical space of polymer degradation products. The diverse modifications of the end structures of oligomers are impressive. They exhibit distinct ADMET properties, as indicated by previous studies²⁸ and the machine learning model predictions in our research. In addition, dozens of polymer oligomers with end structures modified by biological nucleophiles have been annotated using in vitro models. Endogenous small molecules, such as cysteine, can bind to the ends of oligomers, thereby influencing their behaviour in the biological environment.

The discovery of the covalent binding of OLA to intracellular proteins is of great concern. PLA is not only one of the most widely used biodegradable polymers but has also been specifically developed and approved for clinical use³⁷. As documented in previous studies, many MPs have been discovered to enter the human bloodstream and penetrate deep tissues^{38, 39}. Oligomers with lower MW may demonstrate a higher likelihood of widespread distribution; however, their biological binding capacity remains unknown. Our findings suggest a possible mechanism for the cytotoxicity of MPs and oligomers^{40, 41}.

Therefore, oligomers can be recognized as a new class of pollutants. Current knowledge of the ecological effects and toxicity of oligomers is far from sufficient. The result of this study has greatly environmental implication by increasing our understanding the fate of the polymer in the environment or human, which could be used to guide its future use policy and design.

Structure identifiers of oligomer.

The identification of oligomer structures presents a knowledge gap in the understanding of the relationship between oligomers and their parent polymers. This gap poses challenges in screening and identification processes using MS⁴². In this context, the molecular structure and formula of an oligomer can be deconstructed using its RU, DP, and EG (Eq. 1) ⁴³. Notably, EGs for polyolefin polymers may not exist and may involve complex free-radical reaction products⁴⁴.

$$Oligomer=RU*DP+EG \left(1\right)$$

Typically, homopolymers composed of a single type of monomer are the most common, and RU primarily consists of monomer residues³⁵. The structure is expressed as a monomer when the DP is 1 (Eq. 2).

$$Monomer=RU+EG \left(2\right)$$

For other polymerization patterns such as copolymers and block polymers, the relationship between RU and the monomer became more intricate (details in Supplementary Dataset S1).

Polymer oligomer database construction.

Owing to the lack of data openness in polymer chemical databases and the industry's confidentiality of synthetic pathways, a shared and collaborative oligomer database, the polymer oligomer database (PODB), was created using basic polymer information from the published literature and professional websites. The Polymer Database (www.polymerdatabase.com) was used to provide a database of polymers with information on their chemical structure, properties, and processing methods. The database includes 169 polymers across 27 classes, such as nylon66-poly (caprolactam) (PA66) in "Polyamides" and polyglycolide (PGA) in "Biodegradable Polyesters." Another comprehensive database, PlasticMAP⁵ comprising 480 monomers from 15 types of plastics, along with the corresponding partial yield and toxicity information, was utilized. After manual examination, 240 monomers with available MW and polymer information were selected for inclusion. More information, including the formulas for some of the monomers and NLs, was inferred from the synthesis route. Because this information source has limited structural details, hindering accurate identification, the oligomer database was provided as a reference in Supplementary Dataset S3, which was not connected to Oligomer-Finder. Although LC-MS may face challenges in detecting the oligomers of polyolefin polymers owing to their lower polarity, we included RU to account for potentially highly polar modified oligomer structures⁴⁵. Notably, only one possible EG consistent with the parent polymer and its homologues was considered in this database^{46, 47}.

In the case of OLA_4H,OH, we demonstrated how to use MS information to express the features of the oligomers and their associated structural information (Supplementary Fig. S7a). CurlySMILES was used to express polymer structures in a computer language⁴⁸ that was developed to express oligomer structures. Formula information was obtained using the ChemmineR package, and accurate mass information was acquired through a search using the Xcalibur software. Finally, the information was summarized for both the parent polymer and oligomer, including curlySMILES, RU, and EG. The PODB, which contains 171 RUs in synthetic polymers and is utilized in Oligomer-Finder, is presented in Supplementary Dataset S2, with a further detailed description provided in Supplementary Fig. S7c. Owing to the lack of toxicity and yield information on oligomers, their corresponding monomers have been extensively studied. Furthermore, we modified PlasticMAP to offer noteworthy high-risk monomers, as presented in Supplementary Dataset S4. Importantly, PODB is open to editing, and the addition of other oligomers is welcome, as it enhances the confidence level of the screened oligomers.

Confidence degree in the assignment of seed oligomer candidates

Oligomer-Finder employs a multistep strategy to ensure comprehensive and efficient oligomer screening. The first module, Seed Oligomer-Finder, automatically generates seed oligomer candidates. However, some candidates exhibited low confidence, were irrelevant, or were homologues of the same parent polymer. To improve the efficiency of oligomer screening, the seed oligomer candidates were divided into 4 confidence degrees and manually selected the authentic "seed oligomer":

(1) Degree 4: Precursors exhibiting rNL in the MS2 spectra.

(2) Degree 3: Calculated DP was greater than 1.

(3) Degree 2: The rNL values for the seed oligomer candidates were available in the PODB. Candidates at this level are polymer congeners.

(4) Degree 1: Both the rNL and EG values were included in the PODB. Candidates at this level are typically polymer homologs.

Candidates at Degree 3 or 4 may include bio-oligomers, such as short peptides, nucleic acids, fatty acids⁴⁹, and synthetic substances, such as per- and polyfluoroalkyl substances (PFASs), for which RUs have been reported in the MS2 apectra⁵⁰. At Degree 2, the accurate mass of the rNL is denoted as "Reference_NL" and included in the "Seed oligomer-Finder" results. For Degree 1, the EG value is provided as "Reference_EG." Additionally, an accurate precursor m/z, named "Reference_Precursor," can be calculated using equations 2 and 3. Furthermore, it is important to note that some MS interferences with distinct RUs can also be identified by the "Seed oligomer-Finder" and above Degree 2. Supplementary Dataset S5, adapted from Keller et al.⁵¹ compiles common RU and their origins in various MS ionization modes to assist in the elimination of interference. A single seed oligomer with a unique RU represents oligomer contamination from the polymer source. Usually, only one representative of the Degree 1 seed oligomer derived from a type of polymer is selected for subsequent analysis to reduce the redundancy of the screening and results.

Homologue screening

For homologues, their mass relationship is defined by Eq. (3):

$$Homologue 1-Homologue 2=RU*\left(DP 1-DP 2\right) \left(3\right)$$

An algorithm was crafted as "Homologue-Finder" in R, guided by Eq. (4):

$$1-\frac{Homologue-seed oligomer}{\left(rNL*round\left(\frac{Homologue-seed oligomer}{rNL}\right)\right)} \le Mass error \left(4\right)$$

Two mass-error values were used in the analysis. "Mass error1" was calculated based on the accurate masses of the precursor and rNL in the seed oligomer in the PODB, contingent on the MS1 resolution of the instrument (e.g., 5 ppm for Orbitrap MS). "Mass error2" is utilized for seed oligomer at Degree 3 or 4, and a value of 30 ppm was optimized to minimize error accumulation.

Congener screening

In the case of OLA_4α,ω, we identified six distinct EGs corresponding to these congeners (Fig. 4c). Five EGs have been documented in the literatures^{23, 52}, while others (hydrogen group for OLA_nH,H and methoxy group for OLA_nH,MeO) were found in Degree 2 seed oligomer candidates and are reported for the first time. The end structure was inferred from the MS2 spectra (Supplementary Fig. S8). These congeners are believed to participate in processes such as intramolecular transesterification (cyclic [LA]₄)⁹ and alcoholysis involving compounds such as MeOH (CH₃-O-[LA]₄-H).

As previously mentioned, congeners should receive special attention as a large class of oligomers. For congener screening, we first assumed that every MS1 peak was a congener of seed oligomer and calculated the possible EG accurate mass by Eq. (5) modified from Eq. 1:

$$round(Congener-rNL*n)\le Mass error \left(5\right)$$

where n is the number of possible RU in the congener. Correspondence between the possible mass and EG mass within the OEGDB (Supplementary Dataset S6) was then evaluated. If the mass deviation fell within the specified mass error range (e.g., 5 ppm for Orbitrap MS), the hypothesis was considered valid. Considering the importance of nucleophiles in the environment, there is a supplementary database named OEGDB-env of potential 15 EGs⁵³ (Supplementary Dataset S7). It has been reported that MPs and oligomers can enter the human body or other organisms⁵⁴ which makes it possible for oligomers to react with biological nucleophiles. A supplementary OEGDB-bio with potential 30 EGs in organisms was developed (Supplementary Dataset S8).

Polymer oligomer solution preparation

Chemicals and materials used in this study were shown in Supplementary MeOH was chosen as the solvent to obtain oligomers from polymers as it did not dissolve any of the polymers⁵⁵. The detailed procedure was provided in Supplementary. Prof. Xiangcheng Pan's laboratory at Fudan University provided 4 distinct well defined OLA_nH,OH (n = 2, 4, 6, 10) via their previous study¹⁸ (Supplementary Fig. S14).

Water leaching experiments of MPs

Due to limited information on MP levels in the aquatic environment, the concentrations of the prepared MPs in ultrapure water were set at 0 (control) and 5 g/L based on a previous study⁵⁶. The leaching conditions were established following the protocols outlined by Jiang et al.⁵⁷ with slight modifications. To emulate MP pollution under surface water condition, 2 g of each of the five MPs (PLA, PHB, PCL, PBS and PA6 were added to 400 mL of Milli-Q water (Millipore Corporation, MA, USA). The detailed procedures were described in Supplementary The samples used for oligomer annotation were pooled from each experimental group of samples separately⁵⁸.

Incubation of MPs with cysteine

A recent study based on the MPs introduced during food processing are considered, the intake of MPs will reach 12–38 mg/day^{59, 60}. The incubation of four mixed biodegradable MPs (PLA, PHB, PCL, and PBS) with cysteine was detailed in Supplementary.

LC-HRMS analysis of oligomer samples

The platform consisted of a Vanquish high-performance liquid chromatography (HPLC) system and a Q Exactive Plus mass spectrometer (Orbitrap MS) equipped with a heated electrospray ionization (HESI) source, all from Thermo Scientific. Detailed settings for the MS parameters can be found in Supplementary.

MS data pre-analysis for Oligomer-Finder

The primary data analysis was performed using Oligomer-Finder, which necessitates the import of an MS1 peak table (in txt format) and all MS2 data files (in mat format). The MS1 peak table comprises peak information such as m/z and RT, generated from the raw MS files using common peak picking software like MS-DIAL. Detailed settings for the MS data pre-analysis can be found in Supplementary.

Polymer oligomer data analysis using Oligomer-Finder

An analysis of a water-leached sample containing MPs was conducted using Oligomer-Finder. The exemplary results of the analysis obtained using the "Seed Oligomer-Finder" module are presented in Supplementary Fig. S15. Meticulous selection of seed oligomers with a high degree of confidence forms the basis for subsequent neighbour oligomer screening. After employing homologue screening, RT prediction, and structure annotation by PODB to these Degree 1 seed oligomers in "Homologue-Finder," an illustrative example of the identified homologues has been shown in Supplementary Fig. S15. Furthermore, we compared the performances of Oligomer-Finder and MS-DIAL⁶¹ using functional identification with a spectral database of 15,245 unique compounds in negative mode (MSMS-Public-Neg-VS17) for oligomer annotation. Oligomer-Finder successfully annotated all 31 oligomers (< 1000 Da) in OLA_nH,OH, OHB_nH,OH, and OCL_nH,OH. However, MS-DIAL only annotated the OLA_2H,OH and OHB_2H,OH formulas, providing inaccurate structures. This is easily understandable, as out of the 31 compounds, only 10 were documented in PubChem, and their MS2 spectra were unavailable because of the absence of corresponding chemical standards. The R script "Congener-Finder" was devised to screen the congeners whose EGs are included in the OEGDB. The illustrative result of OLA congeners using "Congener-Finder" have been detailed in Supplementary Fig. S17.

Cells exposed to OLA

The HepG₂ cells in 6-well plates were treated with 0.5% DMSO or 500 µM OLA produced by PLA leachate upon reaching 50 ~ 60% confluence. Details of the cell exposure assay, protein extraction, and proteomic analysis are described in Supplementary.

Identification of OLA modified proteins

Peptides and proteins were identified using Proteome Discoverer (version 3.1; Thermo Scientific) by searching the protein database uniprotkb_ Human_ AND_ reviewed_ true. Three possible OLA_nH,oH (n = 2–4) modified molecular compositions (Supplementary Table S5) were input into Proteome Discoverer as variable modifications, and the [LA], rNL of OLA was set as the possible NL. The other related parameters are described in Supplementary.

Code availability

Oligomer-Finder was developed using R, and Oligomer_Finder_UI was based on Qt Creator software. The source code and GUI version of Oligomer-Finder can be found and downloaded for scientific research purposes at https://github.com/FangLabNTU/Oligomer-Finder. A help document and the demo data for learning were also available.

Data availability

All data are included in the manuscript and/or Supplementary.

Acknowledgments

This work was sponsored by the National Natural Science Foundation of China (22376032), Strategy Priority Research Program (Category B) of Chinese Academy of Sciences on the New Pollutants, National Key R&D Program (2022YFC3702600 and 2022YFC3702601), and Startup Grant of Fudan University (JIH1829010Y). Dr. Mingliang Fang was further supported by Agilent University Relations: ACT-UR Program and Grant ID #4863 and "Xiaomi" young investigator award. The authors would like to thank J. Xu from Fudan University and J. Jiang for their assistance in finalizing the code and developing the GUI version. We also acknowledge X. Pan from Fudan University and W. Jiang from Nanjing University for professional discussion and synthesis experiments.

Author information

Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China

Changzhi Shi, Xing Chen, Jing Yang, Ao Guo, Feng Zhao, Ailin Zhao, Xiaojia Chen, Zimeng Wang, Jianmin Chen & Mingliang Fang

West Coast Metabolomics Center, University of California−Davis, Davis, California 95616, United States

Min Liu

Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco (UCSF), San Francisco, California 94143, USA

Mengjing Wang

Key Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

Fengbang Wang & Maoyong Song

School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, 639798, Singapore

Mingliang Fang

Contributions

C.S. and M.F. conceived and designed the study. J.C., and M.F. supervised the study. C.S., A.Z., and X.C. performed the HepG2 cellular experiments and data analysis. C.S., A.G, and J.Y. conducted the mass spectrometry experiments. C.S., M.L., and XJ.C. authored the R and GUI scripts. C.S., and J.Y. collated the polymer and oligomer information for developing databases. M.W., Z.W., F.W. and M.S. provided advice on experimental design, and helped modify all subsequent drafts. C.S., F.Z., and M.F. wrote the manuscript with input from all the authors. All the authors discussed the results.

Corresponding author

Correspondence to Jianmin Chen or Mingliang Fang.

Ethics declarations

Competing interests

The authors declare no competing interests.

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Seed Molecule-Guided Strategy Facilitates the Precise Identification of "Hidden" Polymer Breakdown Products

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Abstract

Figures

Introduction

Results

Discussion

Methods

Confidence degree in the assignment of seed oligomer candidates

Homologue screening

Congener screening

Polymer oligomer solution preparation

Water leaching experiments of MPs

Incubation of MPs with cysteine

LC-HRMS analysis of oligomer samples

MS data pre-analysis for Oligomer-Finder

Polymer oligomer data analysis using Oligomer-Finder

Cells exposed to OLA

Identification of OLA modified proteins

Declarations

References

Additional Declarations

Supplementary Files

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