Nontarget screening based on molecular networking strategy to identify transformation products of citalopram and sertraline in wastewater

Dissolved organic matter (DOM) sustains a substantial part of the organic matter transported seaward, where photochemical reactions significantly affect its transformation and fate. The irradiation experiments can provide valuable information on the photochemical reactivity (photolabile, photoresistant, and photoproduct) of molecules. However, the inconsistency of the fate of irradiated molecules among different experiments curtailed our understanding of the roles the photochemical reactions have played, which cannot be properly addressed by traditional approaches. Here, we conducted irradiation experiments for samples from two large estuaries in China. Molecules that occurred in irradiation experiments were characterized by the Fourier transform ion cyclotron resonance mass spectrometry and assigned probabilistic labels to define their photochemical reactivity. These molecules with probabilistic labels were used to construct a learning database for establishing a suitable machine learning (ML) model. We further applied our well-trained ML model to "unmatched" (i.e., not detected in our irradiation experiments) molecules from five estuaries worldwide, to predict their photochemical reactivity. Results showed that numerous molecules with strong photolability can be captured solely by the ML model. Moreover, comparing DOM photochemical reactivity in five estuaries revealed that the riverine DOM chemistry largely determines their subsequent photochemical transformation. We offer an expandable and renewable approach based on ML to compatibly integrate existing irradiation experiments and shed insight into DOM transformation and degradation processes.

Section: Resultsmentioning

confidence: 99%

Exploring the Complexities of Dissolved Organic Matter Photochemistry from the Molecular Level by Using Machine Learning Approaches

Zhao

Chen

et al. 2023

“…The prioritized candidate TPs obtained through nontarget screening had much fewer false positives due to the inclusion of MS/MS spectra. 38 However, the performance of nontarget screening greatly depends on the quality and availability of the MS/MS data. When conducting nontarget screening, there is a possibility of excluding true positives (TPs) that lack MS/MS data or have noisy MS/MS data.…”

Section: Resultsmentioning

confidence: 99%

“…Compared to suspect screening, nontarget screening, which uses molecular networking and diagnostic fragment searching, played a significant role in identifying unknown TPs. The prioritized candidate TPs obtained through nontarget screening had much fewer false positives due to the inclusion of MS/MS spectra . However, the performance of nontarget screening greatly depends on the quality and availability of the MS/MS data.…”

Section: Resultsmentioning

confidence: 99%

“…With these strategies, extensive studies have primarily focused on the transformation of some specific antibiotics in lab-scale experiments, and successfully identified many TPs and proposed relevant transformation pathways. ,,,− Some studies have also screened the target and suspect antibiotic TPs in WWTPs and natural waters, such as macrolide TPs in WWTP influents and effluents, , 9 sulfonamide TPs in river water, and TPs of different micropollutants (including antibiotics) in WWTP effluents. , However, in most cases, only some well-known antibiotic parents and TPs were included in the screening scope, with the limited added value of the truly unknown TPs in the environment. The complete profiles of antibiotic-related compounds in WWTPs and their hazards and ecological risks were poorly understood, which are critical for pollution control and risk management of antibiotics.…”

Section: Introductionmentioning

confidence: 99%

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Suspect and Nontarget Screening Reveal the Underestimated Risks of Antibiotic Transformation Products in Wastewater Treatment Plant Effluents

Hu,

Lyu,

Chen

et al. 2023

Antibiotics are anthropogenic contaminants with a global presence and of deep concern in aquatic environments, while less is known about the occurrence and risks of their transformation products (TPs). Herein, we developed a comprehensive suspect and nontarget screening workflow based on high-resolution mass spectrometry to identify unknown antibiotic TPs in wastewater treatment plant effluents. We identified 211 compounds (35 parent antibiotics and 176 TPs) at confidence levels of ≥3 and 107 TPs originated from macrolides. TPs were quantified by 17 TPs standards and semiquantified by the predicted response factors and accounted for 55.6−95.1% (76.7% on average) of the total concentrations of parents and TPs. 22.2%, 63.1%, and 18.8% of the identified TPs were estimated to be more persistent, mobile, and toxic than their parent antibiotics, respectively. Further ecological risk assessment based on concentrations and toxicity to aquatic organisms revealed that the cumulative risks of TPs were generally higher than those of parents. Despite the newly formed N-oxide TPs, the tertiary treatment process (mainly ozonation) could decrease the averaged 20.3% of concentrations and 36.2% of the risks of antibiotic-related compounds. This study highlights the necessity to include antibiotic TPs in environmental scrutiny and risk assessment of antibiotics in different aquatic environments.

“…These methods were initially designed for the untargeted identification of natural products . Until recently, the methods have been extended for inferring the transformation products and pathways of specific pollutants (e.g., sertraline and citalopram) in wastewater . However, since similarity itself is hard to interpret, querying the spectra against databases to identify known molecules within the network is requisite to infer the associated reactions .…”

Section: Introductionmentioning

confidence: 99%

Deciphering Microbe-Mediated Dissolved Organic Matter Reactome in Wastewater Treatment Plants Using Directed Paired Mass Distance

Wang,

Lin,

et al. 2023

Self Cite

Understanding the reaction mechanism of dissolved organic matter (DOM) during wastewater biotreatment is crucial for optimal DOM control. Here, we develop a directed paired mass distance (dPMD) method that constructs a molecular network displaying the reaction pathways of DOM. It couples direction inference and PMD analysis to extract the substrate−product relationships and delta masses of potentially paired reactants directly from sequential mass spectrometry data without formula assignment.Using this method, we analyze the influent and effluent samples from the bioprocesses of 12 wastewater treatment plants (WWTPs) and build a dPMD network to characterize the core reactome of DOM. The network shows that the first step of the transformation triggers reaction cascades that diversify the DOM, but the highly overlapped subsequent reaction pathways result in similar effluent DOM compositions across WWTPs despite varied influents. Mass changes exhibit consistent gain/loss preferences (e.g., +3.995 and −16.031) but different occurrences across WWTPs. Combined with genome-centric metatranscriptomics, we reveal the associations among dPMDs, enzymes, and microbes. Most enzymes are involved in oxygenation, (de)hydrogenation, demethylation, and hydration-related reactions but with different target substrates and expressed by various taxa, as exemplified by Proteobacteria, Actinobacteria, and Nitrospirae. Therefore, a functionally diverse community is pivotal for advanced DOM degradation.