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Article

Suspect and Target Screening of Natural Toxins in the Ter River Catchment Area in NE Spain and Prioritisation by Their Toxicity

by
Massimo Picardo
1,
Oscar Núñez
2,3 and
Marinella Farré
1,*
1
Department of Environmental Chemistry, IDAEA-CSIC, 08034 Barcelona, Spain
2
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, 08034 Barcelona, Spain
3
Serra Húnter Professor, Generalitat de Catalunya, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Toxins 2020, 12(12), 752; https://doi.org/10.3390/toxins12120752
Submission received: 5 October 2020 / Revised: 23 November 2020 / Accepted: 26 November 2020 / Published: 28 November 2020
(This article belongs to the Special Issue Detection and Current Challenges in Marine and Freshwater Biotoxin Monitoring)
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Abstract

:
This study presents the application of a suspect screening approach to screen a wide range of natural toxins, including mycotoxins, bacterial toxins, and plant toxins, in surface waters. The method is based on a generic solid-phase extraction procedure, using three sorbent phases in two cartridges that are connected in series, hence covering a wide range of polarities, followed by liquid chromatography coupled to high-resolution mass spectrometry. The acquisition was performed in the full-scan and data-dependent modes while working under positive and negative ionisation conditions. This method was applied in order to assess the natural toxins in the Ter River water reservoirs, which are used to produce drinking water for Barcelona city (Spain). The study was carried out during a period of seven months, covering the expected prior, during, and post-peak blooming periods of the natural toxins. Fifty-three (53) compounds were tentatively identified, and nine of these were confirmed and quantified. Phytotoxins were identified as the most frequent group of natural toxins in the water, particularly the alkaloids group. Finally, the toxins identified to levels 2 and 1 were prioritised according to their bioaccumulation factor, biodegradability, frequency of detection, and toxicity. This screening and prioritisation approach resulted in different natural toxins that should be further assessed for their ecotoxicological effects and considered in future studies.
Keywords:
natural toxins; cyanotoxins; phytotoxins; mycotoxins; suspected screening; HRMS
Key Contribution: A suspect screening approach has been applied to assess natural toxins in one of the water reservoirs of Barcelona city, NE Spain. The toxins that were tentatively identified were prioritised.

1. Introduction

Natural toxins in the aquatic ecosystem can be produced by different organisms, including bacteria, plants and fungi, thus grouping together a wide variety of structures and physicochemical properties and effects [1]. The risk of water contamination by natural toxins generates environmental and public health issues. In some cases, natural toxins can be accumulated in aquatic organisms and transferred throughout the aquatic food chain to humans [2].
However, if we consider freshwater environments, the primary route of human exposure includes the consumption of contaminated water, dermal exposure, and inhalation during recreational activities. Intoxication can include different symptoms, such as a severe headache, a fever, and respiratory paralysis, as well as a variety of possible effects that include hepatotoxicity, neurotoxicity, carcinogenicity, and dermal toxicity. Due to their diversity, toxicological assessment is still challenging and there is also an information gap concerning their occurrence, due to the lack of analytical methods and certified standards. Therefore, the concentration of natural toxins in drinking water for most of these groups is not yet well regulated, and this is also of concern for countries in the European Union (EU).
Among the natural toxins, the cyanotoxins group is one of the most studied groups in freshwater ecosystems. Cyanotoxins can be released by cyanobacterial blooms, which is a frequent natural phenomenon that is characterised by an algal biomass accumulation in surface water. These secondary metabolites include hepatotoxins (microcystins and nodularins), neurotoxins (such as anatoxins, saxitoxins, and β-methylamino-l-alanine), cytotoxins (such as cylindrospermopsin), and dermatotoxins (lipopolysaccharide, lyngbyatoxins, and aplysiatoxin). Among them, microcystins (MCs), produced by freshwater cyanobacteria genera such as Microcystis, Aphanizomenon, Planktothrix, Dolichospermum, etc. [3], are the most diverse group and the best described in the literature [4]. However, only one congener is regulated. The World Health Organization (WHO) has issued a guideline value of 1 µg/L in drinking water for microcystin-LR (MC-LR), which is one of the most toxic and widespread toxins in water supplies [5].
Another relevant group is represented by mycotoxins, which are secondary metabolites produced by fungi. Due to their diverse chemical structures, mycotoxins can present a wide range of toxicity, such as hepatotoxicity, nephrotoxicity, neurotoxicity, and immunotoxicity, and some of them have been recognised as being teratogenic, mutagenic, and carcinogenic [3]. Their biological effects have been extensively reported and regulated in food and feed [6,7] but not in water. However, many environmental species (particularly of the genus Aspergillus) show resistance to the commonly used water disinfection procedures, allowing them to enter water distribution/reticulation systems [8,9]. Moreover, those species can form mixed biofilm communities with bacteria, algae, and protozoa. These biofilms increase the ability to survive heat treatments and chlorination procedures. Therefore, fungal presence in tap water distribution systems also leads to an increase in the presence of temperature-tolerant fungi, which are the target of many studies that note this as a serious health risk [10].
The phytotoxins group includes secondary metabolites that are produced by plants as a defence mechanism against herbivores, insects, or other plant species [11]. They can include different chemical structures, including peptides, terpenoids, flavones, glycosides, and phenolic compounds (<3500 Da) [12]. Phytotoxins can be grouped into three major chemical structures: alkaloids, terpenes, and phenols. Among them, furanocoumarins, lectins, glycoalkaloids, and pyrrolizidine alkaloids are the most studied [1,13,14]. These compounds can end up in water bodies due to leaching from leaves and soil, and some of them can present high toxicity, such as the case of the carcinogenic ptaquiloside, which is produced by bracken fern [15]. However, in general, few studies have explored their presence in surface waters [16], despite their potentially high toxicity alone or in combination with other anthropogenic contaminants.
During the recent decades, the contamination and over-enrichment of nutrients (eutrophication) of surface waters have increased the number of harmful algal bloom events. Moreover, the increasing temperatures and light intensity promote the algal bloom events and consequently the production of natural toxins [17]. Their chemical diversity, the variety of their structures with structural features that are comparable to common anthropogenic contaminants, and their low concentrations can lead to harmful effects, making their determination in surface waters a great challenge. For these reasons, it is of primary importance to investigate the occurrence of natural toxins in the aquatic environment.
The most common approaches using multi-residue analysis include a limited number of compounds [18,19]. Most approaches cannot determine a wide range of polarities, in that they are mostly applied for one particular compound or a group of compounds with similar characteristics. The suspect screening methods that are based on high-resolution mass spectrometry (HRMS) opened a new window for the comprehensive study of natural toxins in surface waters.
In this regard, the main goal of the present study was to apply a recently developed method [20], based on a generic three-step solid-phase extraction (SPE) procedure followed by liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS), with full-scan (FS) and data-dependent MS2 (DDA) acquisition using a Q-Exactive Orbitrap analyser, to study the natural toxins in different water reservoirs that are used to produce tap water in Barcelona city (Catalonia, NE Spain).
Here, we present the data that was originated by the analysis of a complete set of samples that were collected during a sampling campaign in the period of March to September 2018. The data reported in the previous work have been omitted in the present one. In this sampling campaign, the 48 samples were collected at 4 sites along the Ter River. Sample collection was carried out twice a month from March to September 2018. In our previous study, the 16 samples that came from the Ter River were collected using a different sampling campaign, specifically designed to assess the good performance of the newly developed approach, and was carried out in May and July, and thus needless to say at different days from the samples presented here. Moreover, a prioritisation protocol, including a scoring system, is reported now, designed to elucidate the most significant natural toxins of concern in the drinking water reservoirs.
The suspect screening was carried out using a suspect list containing 2384 items of natural toxin data that were collected from the literature and online databases (mzCloud and ChemSpider). The confidence levels for the identification of suspect natural toxins were based on the approach that was previously reported by Sckymansky et al. [21], consisting of mass accuracy, isotopic fit, fragmentation, and final confirmation, using standards and retention times. Finally, the suspect natural toxins were prioritised according to their toxicity, frequency of detection, biodegradability, and bioaccumulation factors. The results of this screening and prioritisation protocol present a set of natural toxins that could be assessed for their toxicological effects and should also be considered in future water monitoring studies. To the best of our knowledge, this is the first study providing the prioritisation of natural toxins in a water reservoir in Spain.

2. Results and Discussion

2.1. Tentatively Identified Compounds

In this study, after removal of the background and the very small signals under the minimum intensity threshold, 4404 suspect masses were detected in the 48 water samples by using Compound Discoverer 3.1 software. Among them, 381 compounds (8.6%) were assessed as suspect natural toxins that were included in the in-house database and finally selected for further screening. It is noteworthy that the compounds of the study were natural toxins pertaining to three major groups in water, phytotoxins, mycotoxins, and cyanotoxins. Other compounds, such as pesticides, were discarded in this study. Among these 381 structures, after filtering by way of the isotopic patterns, ionisation efficiency, and fragmentation patterns, the number of suspected identified compounds diminished to 191 structures (50.1% of the initial potential for natural toxins). Finally, the comparison with in-silico MS2 patterns gave 50 structures that were tentatively identified at level 2 (25.7% of the initial potential for natural toxins) (Table 1 and Figure 1). Finally, nine natural toxins were confirmed and quantified by injections of the standard.
Plant toxins were the most prominent group in the studied samples (73% of the tentatively identified compounds), with a prevalence of the alkaloids group. The most frequently identified phytotoxins were acetoxytropane, retronecine, and N-methyl pseudo conhydrine in 71%, 70%, and 46% of the samples, respectively. These results are in agreement with the diversity of endemic plants of the area [22], due to the different climatic zones of the occidental Pyrenees and the variation in dry and wet periods. The occurrence of some of these toxins was at a maximum in April, May, August, and September. These two peaks of natural toxins can be related to the leaching into the water immediately after the flowering period in the Mediterranean area, corresponding to April and May, and posteriorly the release of toxins from the dead plant with the consequent rain-washing effect into the river in August and September. For example, in Figure 2, the intensity of the signals of three alkaloids, acetoxytropane, anethole, and retronecine, which can be attributed to the Symphytum officinale, Pimpinella anisum [23], and Apiaceae families, are displayed. As can be seen, the maximum intensities of the toxins were between May and September. In addition to the alkaloids, some terpenes were also tentatively identified. A common species in this area and in the general region of the Iberian Peninsula is bracken (Pteridium aquilinum) [24], which produces ptaquiloside [15]. Ptaquiloside is a carcinogen norsesquiterpene glucoside that is responsible for haemorrhagic disease and bright blindness in livestock and can produce gastric cancer in humans [25]. As can be seen in Figure 1, in this study the degradation product of ptaquiloside, ptaquilosin B (PTB) [26], was identified in 33% of the samples, while ptaquiloside was not detected. The degradation of ptaquiloside in soils and the start of the rainy season explains the leaching of PTB into the water, which is coincident with the maximum intensities of the signals in the samples that were collected in August and September (Figure 3). Another relevant group of phytotoxins, the phenolic group, was less represented in the samples that were identified, and the representatives of this group were present in a minor number of samples. An example was p-coumaric acid, which was found in only 8% of the samples.
Mycotoxins were marginally detectable in the samples, and 58% of the studied water samples did not present detectable concentrations. Alpha-zearalenol was the most prevalent suspect mycotoxin with an occurrence of 29%, followed by aflatoxin B2 (25%), aflatoxin B1 (12%), and averufin, which is an anthraquinoid precursor of aflatoxins [27,28]. Regarding the distribution during the study period, mycotoxins were almost exclusively detected in August and September when the rainy season started, indicating that their presence in water could be due to the washing effect of plants infected with Aspergillus flavus and Aspergillus parasiticus in the case of aflatoxins and Fusarium mycotoxins in the case of alpha-zearalenol. As can be seen in Figure 2, and on the principal component analysis (PCA) presented in Figure 4, the occurrence of natural toxins in natural waters is influenced by seasonality, and the months with a higher charge of natural toxins were in this case April, August, and September, while a very low presence of natural toxins was found at the end of winter and during the driest months. Contrary to what can be expected, the samples from May and July were almost free of cyanotoxins. Only in M1 and M2 during April, August, and September was the occurrence of cyanotoxins detected, in agreement with the two peaking algal blooms in the Mediterranean region. This site (M1) corresponded to the area of Pasteral dam, which is the reservoir that is located downstream of the other reservoirs and presenting slightly higher levels of eutrophication in comparison with the other three areas. The more frequently found cyanotoxins were anatoxin-a, which was present in four samples, followed by microcystin LR, LW, and YR.
The concomitant presence of three MCs, both with anatoxin-a, at the sampling point M1, suggests this area is of a higher risk in terms of the occurrence of MCs, and therefore of MC producers. This is in line with the previous studies reporting benthonic species in the NE of Catalonia. Thirty-two different species have been identified as endemic in this area [29]. Toxins producing genera of freshwater cyanobacteria include Phormidium spp., Oscillatoria spp., Nostoc spp., and Pseudanabaena spp. [27]. These were considered to be the main producers of MC-LR, MC-YR, and –LW found in the M1 point in May and July. The occurrence of cyanotoxins can be related to increments in temperature and eutrophication, as was confirmed by the Catalan Water Agency [28] and CARIMED 2018 [30] for this area during the period studied. On the other hand, M1 is the downstream point of the studied area, which receives nutrients from areas in the upper river, with nitrate levels between 0.67 and 10 mg N-NO3−/L.

2.2. Target Analysis

A target analysis of 27 natural toxins was carried out using certified standards that are summarised in Table A1 of Appendix A. Matrix-matched calibration curves were used for the quantification of eight natural toxins. The limits of detection (LODs) were between 0.002 to 0.4 µg/L while the limits of quantification (LOQs) were between 0.07 and 1.5 µg/L. The analytical parameters are summarised in Table A3. Nine toxins were confirmed (Ana, AflB1, MC-LR, MC-LW, Nod, MC-YR, Kja, 7-methoxycoumarin, and umbelliferone). Concentrations were under the limit of 1 µg/L as proposed by the World Health Organisation [24] and they were used as an arbitrary reference limit in this work. MC-LR was confirmed in only two sampling points (April M1 and September M1), where the precursor ion [M + H]+ 995.5560 m/z was detected for both with the fragment 135.0806 m/z, which is typically generated by the ADDA structure. Finally, MC-LR was confirmed with standards in these two samples. MC-LW and MC-YR were detected at the M1 point in September, August, and, surprisingly, in April, which correspond to the same months where the MC-LR was detected. Anatoxin-a was further detected in the same periods. 7-methoxycoumarin and umbelliferone were confirmed by certified standards. The concentrations of the detected natural toxins are reported in Table 2, showing their presence at relatively low levels in water.

2.3. Prioritisation

In this study, a scoring system was designed to highlight the most significant natural toxins of concern in drinking water reservoirs. The scoring system was in accordance with the previous protocol that was published by Choi et al. [31], which is based on the risk-relevant parameters such as the detection frequency in percentage, biodegradability, log BAF, and the toxicity values based on the 50% lethal dose (LD50) laboratory tests in mice. A score in the range of 0 to 100 for each parameter was used, and 100 points were additionally added if carcinogenicity or neurotoxicity was already reported for the substance as what happens, for example, with AflB1 and AflB2. Thus, the maximum total for a given toxin can be 500. In Table 3, detailed information on the parameterisation and scoring is provided, and in Table 4, the parameters used for each tentatively identified substance are shown. It is noteworthy that the biodegradability and the bioaccumulation factor (BAF), used as log BAF, were calculated using EPI SuiteTM software (United States Environmental Protection Agency, U.S. EPA).
In Table 5, the ranking of the tentatively identified substances is presented. Four substances, namely, tetrahydrocannabivarin, MC-LW, aconosine, and MC-LR, were ranked with more than 300 points, and 13 toxins were ranked with more than 200 points. In this case, it was considered to be the frequency during the sampling period, which includes seasons with a lower incidence of the substances in water.
However, following a month-by-month inspection, for certain substances the frequency was higher; hence, this ranking then varies a little and a higher number of toxins reaches 300 points.
For this reason, in spite of the low concentrations of the substances that are quantified as the top 12 toxins to be tentatively identified, Barcelona city water reservoirs should be monitored at least from May to September, which were the months with higher occurrences of natural toxins.

3. Conclusions

The method described in this article is a good alternative for tentatively identifying suspect natural toxins in surface water. We have shown that the presence of organic matter near the river can potentially cause the leaching of mycotoxins. Moreover, in this study, plant toxins were mostly spread across different points in relation to the presence of different endemic plants. Notwithstanding, the botanical diversity influences the presence of natural toxins as equally as the precipitation and dry periods. The concentrations of natural toxins were not determined due to the lack of certified standards; however, a correlation between the rain and the leaching in water was described and assessed.
Thanks to these results, we report on the importance of the suspect screening for the identification of natural toxins and their final inclusion in prioritisation lists in order to control their presence in water environments, in particular in drinking water reservoirs. It is also important to increase the amount of data, to help scientists identify environmental compounds when no standards are available, or where they are excessively expensive. Many MC congeners are still not included in databases such as MzCloud and Chemspider. Hence, the retrieval of MS2 spectrums for the MC congeners is an issue that is being solved with the efforts of the scientific community via the constant updating of data in dedicated databases for environmental research. For comparison purposes, future works should apply this method of analysing natural toxins across different climates worldwide.

4. Materials and Methods

4.1. Chemicals and Reagents

Twenty-seven (27) natural toxin standards with a maximum purity between 95 and 99% were selected for the targeted analysis. In Table A1 of Appendix A, the list of standards, their main chemical parameters, and providers are listed. Methanol (MeOH), acetone, and acetonitrile (ACN) of HPLC grade were from Merck (Darmstadt, Germany). HPLC water grade was from Baker (Madrid, Spain).

4.2. Samples and Sampling Sites

Forty-eight surface water samples were collected from the Ter River (Catalonia, NE Spain) at four sampling sites: (M1) 41.986133, 2.603488; Point 2 (M2) 41.982191, 2.585539; Point 3 (M3) 41.991090, 2.570144; and Point 4 (M4) 41.975693, 2.395398, in the area of Pasteral, Susqueda, and Sau dams, which are the freshwater reservoirs for Barcelona city tap water.
The sampling was carried out from March to September 2018, except for June, twice per month, in order to study the prior, during, and after blooming periods, when higher concentrations of natural toxins are expected [77]. In each sampling site, the pH, conductivity, and pO2 were measured. Water samples were collected in amber glass bottles that had previously been rinsed, transported at 4 °C, and maintained frozen at −40 °C until the start of the analytical process.

4.3. Sample Pre-Treatment

Sample pre-treatment was based on the generic methodology to isolate natural toxins from water, as recently developed by Picardo et al. [20]. Briefly, each sample was processed in an ultrasonic bath for 20 min to disrupt the microbial cells and to release the intracellular toxins. Then, the sonicated samples were filtered through a glass microfibre filter of GF/B grade (Sigma Aldrich, Steinheim, Germany). Natural toxins were isolated from the filtrate via a three-step solid-phase extraction (SPE) method, using a hand-made cartridge that had been prepared with 200 mg of a porous graphitised carbon (PGC) 120 mesh (Sigma Aldrich, Steinheim, Germany) and 200 mg of a Bond-Elut PPL (PPL) 120 mesh (Agilent, Santa Clara, CA, USA), coupled to an HLB plus cartridge (225 mg sorbent) (Waters Corporations, Milford, MA, USA).
Then, water samples, each of 100 mL, were loaded into the cartridges at a flow rate of 2 mL/min, previously conditioned with 10 mL of MeOH and 10 mL of water, and both solvents were acidified with 0.5% of formic acid (FA). After loading, the cartridges were dried and switched to elute the analytes in the backflush mode. The PGC/PPL cartridge was reversed, while the HLB cartridge maintained the same position. The toxins were eluted with 15 mL of water/MeOH 2:8 (v/v), followed by 15 mL of MeOH and 15 mL of acetone/MeOH 50:50 (v/v). All the solvents were previously warmed at 45 °C before each elution. The eluate was evaporated almost to dryness and re-dissolved in 1 mL of the mobile phase.

4.4. Liquid Chromatography Coupled with High-Resolution Mass Spectrometry

According to the method described by Picardo et al., 2020 [20], the chromatographic separation was carried out using a C18 reversed-phase Lichrosphere (125 mm × 2 mm i.d., 5 μm) column (Merck, Barcelona, ES) connected to an Acquity high-performance liquid chromatography system (Waters Corp, Milford, MA, USA). The binary mobile phase was composed of water (solvent A) and acetonitrile (solvent B) and both had been acidified with 0.1% of FA. The elution gradient was as follows: from 0–3 min, 10% B; from 3–13 min, B was linearly increased to 90%; 13–15 min, stabilised at 90% B; 15–16 min B decreased linearly to 10%; 16–20 min, column stabilisation with 10% of solvent B. A 20 μL injection volume was used with a mobile phase flow rate of 0.25 mL/min.
The HPLC system was coupled to a Thermo Scientific Orbitrap Q-Exactive mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) equipped with a heated electrospray ionisation source (HESI), and used in the positive and negative ionisation modes. The acquisition was performed using a full-scan and data-dependent analysis (FS-DDA) from m/z = 75 to m/z = 1100, with a resolution of 35,000 full widths at half maximum (FWHM) for the FS and 17,500 FWHM for the DDA There was a spray voltage of 3.75 kV (+) and −3.25 kV (−), a sheath flow gas of 20 a.u., an auxiliary gas of 20 a.u., and a sweep gas of 5 a.u. Heater and capillary temperatures were set at 300 °C with an S-lens RF level at 60%. An inclusion list of the 100 most probable suspect compounds was used (Appendix A Table A2).

4.5. Data Processing: Suspect Screening of Natural Toxins

The suspect screening procedure that was previously described by Picardo et al. [20] was employed with minor changes. Briefly, the FS chromatograms that were obtained with the acquisition software Xcalibur Qual Browser (Thermo Fisher Scientific) were processed, using an automated screening with Compound Discoverer software version 3.1 v. x86 (Thermo Fisher Scientific, San Jose, CA, USA). The first screening steps included peak picking, RT alignment, and grouping of isotopes and adducts (to form compounds), as well as the grouping of compounds across samples. Suspect compounds were marked as background if their peak area in the samples was less than three times larger than the maximum peak area in the blanks. Suspects were tentatively identified using the exact mass with a mass error of 5 ppm. This created a first list of suspect compounds that were further filtered by comparison with a homemade database containing the exact mass of more than 2384 natural toxins. Further filtering steps consisted of the comparison of isotopic patterns, ionisation efficiency, and fragmentation patterns. In Figure 5, the general workflow is summarised, which is similar to the workflows of Krauss [78] and Schymanski [21]. Finally, the MS/MS spectrum was compared with the spectrum of a standard or the predicted fragmentation pattern using the ChemSpider and MzCloud online databases. Unequivocal confirmation was only possible when a reference standard was available (identification at level 1).

4.6. Accuracy, Precision, Limits of Detection, and Quantification

Quantification was achieved through calibration curves that were prepared in an artificial freshwater matrix (AFW). The AFW was prepared using the same ingredients that were reported by Lipschitz and Michel [79]. Briefly, the organic matter was simulated with 10 mg/L of technical grade humic acid (Sigma-Aldrich, reference 53,680), and the pH was adjusted to 6.5 with 1.0 M formic acid. Matrix-matched calibration curves were produced using spiked samples from 0.5 to 100 µg/L. Intra-assay precision, accuracy, LOD, and LOQ for the confirmed toxins were calculated according to the EURACHEM guidelines [80]. The instrumental limits of detection (iLOD) were obtained by progressive dilution to the lowest concentration, whereby each compound could be detected. Instrumental reproducibility (inter-day precision) was calculated as the average percentage of the relative standard deviation (RSD%) of the standard solutions (six replicates) at seven concentration levels on three consecutive days.

Author Contributions

Data curation, M.P. and M.F.; Formal analysis, M.P.; Investigation, O.N. and M.F.; Supervision, O.N. and M.F.; Writing—original draft, M.P.; Writing—review & editing, O.N. and M.F. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the research and innovation programme Horizon 2020 of the European Commission under the Marie Sklodowska-Curie grant agreement No. 722493 (NaToxAq), and by the Generalitat de Catalunya (Consolidated Research Groups “2017 SGR 1404—Water and Soil Quality Unit”).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. List of the natural toxin standards employed for the confirmation.
Table A1. List of the natural toxin standards employed for the confirmation.
ToxinToxic GroupChemical FormulaExact MassPurity (%)Supplied by
Microcystin LACyanotoxinC46H67N7O12909.4847>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Microcystin LFCyanotoxinC52H71N7O12985.5160>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Microcystin LRCyanotoxinC49H74N10O12994.5488>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Microcystin LYCyanotoxinC52H71N7O131001.5109>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Microcystin LWCyanotoxinC54H72N8O121024.5269>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Microcystin YRCyanotoxinC52H72N10O131044.5353>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
NodularinCyanotoxinC41H60N8O10824.4432>95Cyano (Cyanobiotech GmbH, Berlin, Germany)
Anatoxin-aCyanotoxinC10H15NO165.2320>98Santa Cruz Biotechnology (Dallas, TX, USA)
CylindrospermopsinCyanotoxinC15H21N5O7S399.121999BOCSci (BOC Sciences, Ramsey Road Shirley, NY, USA)
Aflatoxin B1MycotoxinC17H12O6312.0632>98Merck (Darmstadt, Germany)
Ochratoxin-AMycotoxinC20H18ClNO6403.0823>98Merck (Darmstadt, Germany)
BaicaleinPhytotoxinC15H10O5270.052898Merck (Darmstadt, Germany)
GenisteinPhytotoxinC15H10O5270.0528>98Merck (Darmstadt, Germany)
AmygdalinPhytotoxinC20H27NO11457.158>99Merck (Darmstadt, Germany)
ScopolaminePhytotoxinC17H21NO4303.147>98Merck (Darmstadt, Germany)
CinchoninePhytotoxinC19H22N2O294.1732>98Merck (Darmstadt, Germany)
AtropinePhytotoxinC17H23NO3289.1682>99Merck (Darmstadt, Germany)
Kojic AcidMycotoxinC6H6O4142.0274>98Merck (Darmstadt, Germany)
b-AsaronePhytotoxinC12H16O3208.109970Merck (Darmstadt, Germany)
p-Coumaric acidPhytotoxinC9H8O3164.0471>98Merck (Darmstadt, Germany)
Abietic acidPhytotoxinC20H30O2302.2256>95Merck (Darmstadt, Germany)
7-EthoxyoumarinPhytotoxinC11H10O3190.0634≥97%Merck (Darmstadt, Germany)
7-MetoxycoumarinPhytotoxinC10H8O3176.0479>98Merck (Darmstadt, Germany)
ArbutinPhytotoxinC12H16O7272.0986>98Merck (Darmstadt, Germany)
UmbelliferonePhytotoxinC9H6O3162.0327>99Merck (Darmstadt, Germany)
ThujonePhytotoxinC10H16O152.1235>99Merck (Darmstadt, Germany)
CotininePhytotoxinC10H12N2O176.0956>99Merck (Darmstadt, Germany)
Table
Table A2. Inclusion list of the 100 most probable suspect compounds.
Table A2. Inclusion list of the 100 most probable suspect compounds.
Mass [M + H]+Formula [M]CEToxin and Possible Isomers
239.1542C16H18N235(−)-Agroclavine
180.1019C10H13NO235(−)-Salsolinol, Fusaric acid
398.0961C18H24BrNO435(−)-Scopolamin bromide
128.1433C8H17N35(+)-Coniine
142.1226C8H15NO35(+)-Hygrine
249.1961C15H24N2O35(+)-Lupanine
333.2060C20 H28 O43520-Deoxyingenol
184.1332C10 H17 N O2353-Acetoxytropane
197.1536C12H20O2353-Thujyl acetate
646.3221C34H47NO1135Aconitine
313.0706C17 H12 O670Aflatoxin B1
315.0863C17 H14 O635Aflatoxin B2
329.065C17 H12 O735Aflatoxin G1
331.0812C17H14O735Aflatoxin G2
502.2951C32H39NO435Aflatrem
159.0513C4 H6 N4 O335Allantoin
924.4951C47H73NO1735Amphotericin Bh
458.1656C20H27NO1160Amygdalin
456.1511C20H27NO1135Amygdalin negative
166.1226C10 H15 N O45Anatoxin-A
187.03897C11H6O335Angelicin (Isopsoralen)
504.343C28H45N3O535Antillatoxin
624.3755C34H49N5O635Apicidin
271.0601C15H10O535Apigenin
283.1540C15H22O535Artemisinin
189.1121C9 H16 O435Aspionene
290.1751C17H23NO350Atropine
369.0968C20H16O735Averufin
321.1696C18H24O535a-Zearalenol
261.1597C15H20N2O235Baptifoline
784.4167C45H57N3O935Beauvericin
641.2891C34H44N2O8S35Belladonnine
209.1172C12H16O350beta-Asarone
285.0757C16H12O535Biochanin A (BIO)
438.2638C27H35NO435b-Paxitriol
281.1747C16 H24 O435Brefeldin A
235.1692C15 H22 O235Buddledin B
317.2111C20H28O335Cafestol
195.0876C8H10N4O235Caffeine
153.1273C10H16O35Carveol
261.1849C17H24O235Cicudiol
259.1692C17 H22 O235Cicutoxin
1111.5836C60H86O1935Ciguatoxin
295.1804C19H22N2O35Cinchonine
279.0863C14H14O635Citreoisocoumarin
403.2115C23H30O635Citreoviridin
400.1754C22H25NO635Colchicine
144.1382C8H17NO35Conhydrine
127.0389C6H6O335Coumarin
300.2169C16 H29 N O435Curassavine
225.1961C13H24N2O35Cuscohygrine
416.1234C15H21N5O7S45Cylindrospermopsin
255.0651C15H10O435Daidzein (DAI)
417.1180C21H20O935Daidzin
589.1915C29H32O1335Dalbin
427.1387C23H22O835Dalbinol
249.1485C15H20O335Damsin
291.1227C16H18O535Dehydrocurvularin
355.1176C20H18O635Deoxynivalenol
411.1074C22H18O835Desertorin A
367.1751C19H26O735Diacetoxyscirpenol
765.4419C41H64O1335Digitoxin
415.3206C27H42O335Diosgenin
295.1903C17 H26 O450Embelin
271.0601C15H10O535Emodin
1095.5662C60H74N10O1035Ergoclavin
350.1598C18H23NO635Erucifoline
269.0808C16H12O435Formononetin (FOR)
209.0444C10H8O535Fraxetin
271.0601C15H10O550Genistein or baicalein
155.1430C10H18O35Geraniol
781.4368C41H64O1435Gitoxin
156.1019C8 H13 N O235Heliotridine
304.1543C17H21NO435Hyoscine
143.0338C6H6O435Kojic acid
541.3887C34 H52 O535Lantadene D
358.2012C21 H27 N O435Laudanosine
910.4920C46H67N7O1235MC-LA
995.5560C49H74N10O1235MC-LR
1025.5344C54H72N8O1235MC-LW
1045.5353C52H72N10O1335MC-YR
192.0781C11H12O335Myristicin
825.4505C41 H60 N8 O1035Nodularin
128.1069C7H13NO35Norhygrine
152.0566C5H5N5O35Nostocine
404.0895C20H18ClNO670Ochratoxin-a
215.1277C11H18O435Pestalotin
165.0658C8H8N2O235Ricinine
194.1175C11H15NO235Salsoline
868.5053C45H73NO1535Solanine
746.4837C42H67NO1035Spirolide
183.0288C8H6O535Stipitatic acid
174.11247C8H15NO335Swainsonine
153.1273C10H16O35Thujone
115.0389C5H6O335Tulipalin B
163.0389C9H6O335Umbelliferone
355.2380C22 H30 N2 O235Vincaminorein (Aspidospermine)
203.0338C11 H6 O435Xanthotoxol
Table
Table A3. Calibration curve parameters for the quantification of the confirmed compounds.
Table A3. Calibration curve parameters for the quantification of the confirmed compounds.
ToxinsMolecular Formula[M+H]+Recovery%RSD%LOD µg/LLOQ µg/LR2
AnaC10H15NO166.1234848.00.20.50.989
AflB1C17H12O6416.1242869.90.20.70.999
MC-LRC49H74N10O12995.5568783.30.20.50.995
MC-LWC54H72N8O121025.5342555.80.10.50.991
NodC41H60N8O10825.45129416.20.20.80.992
MC-YRC54H72N8O121045.53618416.90.41.50.943
KjaC12H16O3208.1093856.40.020.080.990
7-methoxycoumarinC10H8O3177.05468270.0020.0070.999
UmbelliferoneC9H6O3163.03897911.20.0090.030.998

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Toxins 12 00752 g001 550
Figure 1. Hits diagram. A dark colour indicates a positive hit.
Figure 1. Hits diagram. A dark colour indicates a positive hit.
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Figure 2. Signal intensities of three alkaloids: acetoxytropane, anethole, and retronecine.
Figure 2. Signal intensities of three alkaloids: acetoxytropane, anethole, and retronecine.
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Toxins 12 00752 g003 550
Figure 3. Ptaquilosin B intensity signals along the sampling period.
Figure 3. Ptaquilosin B intensity signals along the sampling period.
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Figure 4. PCA of the results during the sampling period.
Figure 4. PCA of the results during the sampling period.
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Toxins 12 00752 g005 550
Figure 5. General workflow for suspect screening as proposed by Schymansky et al. [21].
Figure 5. General workflow for suspect screening as proposed by Schymansky et al. [21].
Toxins 12 00752 g005
Table
Table 1. List of suspect compounds (level 2) after tentative identification in the four sampling sites along water reservoirs in the Ter River.
Table 1. List of suspect compounds (level 2) after tentative identification in the four sampling sites along water reservoirs in the Ter River.
ToxinsFormula[M + H]RtMS2 (1)[M-e]+MS2 (2)[M-e]+MS2 (3)[M-e]+MS2 (4)[M-e]+
Plant Toxins
AcetoxytropaneC10H17NO2184.13329.1123.0805C8H11O142.0864C7H12NO2125.0599C7H9O2165.0913C10H13O2
AconosineC22H35NO4378.263911.3283.1701C19H23O2269.1539C18H21O2235.1324C14H19O3137.0599C8H9O2
AnetholeC10H12O149.09619.8115.0544C9H7103.0543C8H7145.065C10H9O121.0649C8H9O
AmbrosinC15H18O3247.13328.5229.1227C15H17 O2201.1267C13H13O2119.0857C9H11
ApiolC12H14O4223.096511.9105.07C8H9119.0857C9H11163.0755C10H11O2149.0963C10H13O
ArabsinC15H22O4266.152110.8249.1488C15H21O3231.1384C15H19O2221.1539C14H21O2
Artemisic acidC15H22O2235.170214179.1069C11H15O2165.0901C10H13O2119.0853C9H11
AspidinolC12H16O4225.11219.5107.0492C7H7O137.0599C8H9O2123.0441C7H7O2109.0649C7H7O
AspidospermineC22H30N2O2355.238012.5107.0492C7H7O136.0759C8H10NO174.0915C11H12NO148.0759C9H10NO
Azelaic acidC9H16O4189.112111.0107.0854C8H11155.0704C8H11O3111.0806C7H11O115.0391C5H7O3
BarnolC10H14O3183.101610.8119.0857C9H11135.0806C9H11O163.0755C10H11O2181.086C10H13O3
Bisabolol oxideC15H26O2239.200612.4133.1013C10H13121.1013C9H13149.1326C11H17187.1483C14H19
Buddledin BC15H22O2235.169312.9113.0598C6H9O2179.0106C11H15O2193.1225C12H17O2155.1067C9H15O2
ConhydrineC8H17NO144.138311.6107.0856C8H11125.0962C8H11O138.0915C8H12NO
CuscohygrineC13H24N2O225.196112.3123.0805C8H11O109.0649C7H9O163.1118C11H15O150.0914C9H12NO
CurassavineC16H29NO4300.216912.6155.0703C8H11O3107.0856C8H11123.0805C8H11O173.081C8H13O4
HerniarinC10H8O3176.047711.8121.0649C7H5O2133.0653C9H9O
Hydroxyarbusculin AC15H22O4267.158513.3159.1169C12H15123.0805C8H11O
HydroxycoumarinC9H6O3163.039015.1121.0284C7H5O2149.0233C8H5O3163.0389C9H7O3105.0335C7H5O
HygrineC8H15NO142.122610.9109.065C7H9O124.0758C7H10NO111.0804C7H11O140.1069C8H14NO
Hypoglycine AC7H11NO2142.08622.3497.0287C5H5O2120.0444C7H6NO124.0757C7H10NO
LaudanosineC21H27NO4358.201313.2121.0285C7H5O2115.0543C9H7159.088C11H11O147.0805C10H11O
LupanineC15H24N2O249.19615.3110.0965C7H12N120.0808C8H10N122.0966C8H12N138.0915C8H12NO
Methyl JasmonateC13 H20 O3225.14850.1107.0855C8H11121.1012C8H13175.112C12H15O165.1275C11H17O
MethylpelletierineC9H17NO156.13862.2107.0705C8H11140.105C8H14NO
MethylpseudoconhydrineC9H19NO158.153911.9107.0856C8H11114.0914C6H12NO123.0805C8H11O109.0649C7H9O
NorpseudopelletierineC8 H13NO140.10709.1109.0649C7H9O121.0649C8H9O138.0917C8H12NO123.0806C8H11O
p-Coumaric acidC9 H8 O3165.054612.5105.07C8H9123.0441C7H7O2133.0649C9H9O125.0598C7H9O2
Ptaquilosin BC14 H20 O3237.148511.2119.0857C9H11159.0807C11H11O145.1013C11H13111.0442C6H7O2
ReticulineC19 H23 N O4330.170013.2115.0543C9H7125.0597C7H9O2145.0646C10H9O135.0441C8H7O2
RetronecineC8 H13 N O2156.10191.9152.0709C8H10NO2118.0652C8H8N114.0916C6H12NO124.0758C7H10NO
SwainsonineC8 H15 N O3174.11258.1140.0682C7H10NO2114.0914C6H12NO125.0598C7H9O2118.0652C8H8N
TetrahydrocannabivarinC19 H26 O2287.200612.9105.07C8H9163.1118C11H15O175.0755C11H11O2217.0123C14H17O2
Tetraneurin AC17 H22 O6323.148912.6281.0996C14H17O6199.0968C19H15O4155.0704C8H11O3213.112C11H17O4
TrachelanthamineC15 H27 N O4286.201312.5155.0704C8H11O3107.085C8H11159.0655C7H11O4215.1269C11H19O4
TussilagineC10 H17 N O3200.128110.6180.1021C10H14NO2165.0912C10H13O2151.0756C9H11O2134.0967C9H12N
UmbelliferoneC9 H6 O3163.039011.1147.0441C9H7O2135.0442C8H7O2111.0441C6H7O2123.0441C7H7O2
VerrucosinC20 H24 O5345.169713.0301.143C18H21O4121.0286C7H5O2141.0548C7H9O3247.1332C15H19O3
XanthotoxolC11H6O4203.03481.3147.1173C9H10O2177.0188C9H5O4173.0239C10H5O3
Mycotoxins
Aflatoxin B1C17H12O6313.070711.2213.0547C13H9O3269.0444C15H9O5285.0761C16H13O5217.0497C12H9O4
Aflatoxin B2C17 H14 O6315.086311.6273.0761C15H13O5255.0654C15H110468.9979C3HO2
Alpha-ZearalenolC18H24O5321.167414.8149.133C11H17121.1016C9H13139.1123C9H15O
Aspergillic acidC12 H20 N2 O2225.15989.4114.0915C6H12NO144.0889C6H12N2O2150.0915C9H12NO128.07C6H10NO2
AverufinC20 H16 O7369.096910.6327.0853C18H15O6299.0555C16H11O6137.0236C7H5O3
Kojic AcidC6H6O4143.03441.38125.0239C6H5O397.02844C5H5O269.0335C4H5O
Cyanotoxins
ANA-aC10H15NO166.12260.5149.1C10H13O131.0859C10H11107.0858C8H11
MC-LRC49H74N10O12995.5569135.0807C9H11O213.087C9H13N2O4375.1914C20H27N2O5
MC-LWC54H72N8O121025.534312135.0807C9H11O376.1926C19H21N10288.1354C17H20O4
MC-YRC52H72N10O131045.53178.9135.0807C9H11O375.1935C19H21N9213.0874C9H13N2O4
NODC41H60N8O10824.44468.6135.0807C9H11O389.2079C21H29NO5691.3795C34H53O10N5
Table
Table 2. Quantification of the confirmed compounds detected in the Ter River.
Table 2. Quantification of the confirmed compounds detected in the Ter River.
ToxinMonthSampling PointConcentration (µg L−1)
Ana-aAprilM10.12
AugustM10.03
SeptemberM10.06
SeptemberM20.28
Afla B1SeptemberM40.9
KjaAprilM40.7
NodSeptemberM10.1
MC-YRApril
August		M1
M1		0.1
0.2
MC-LWAugustM10.4
SeptemberM10.1
MC-LRAprilM10.2
SeptemberM10.7
UmbelliferoneMayM3<LOD
JulyM2
M3		<LOD
0.1
AugustM2
M3		<LOD
<LOD
7-methoxycoumarinMayM2
M3		0.17
0.008
JulyM2
M3		0.08
0.18
AugustM2
M3		0.06
0.03
SeptemberM10.04
Abbreviations: Afla B1: aflatoxin B1; Ana-a: anatoxin-a; Kja: Kojic acid; Nod: nodularin; MC-YR: microcystin-YR; MC-LW: microcystin-LW; MC-LR: microcystin-LR.
Table
Table 3. Scoring system for prioritisation of the quantified substances with the risk relevant parameters (detection frequency, biodegradability, bioaccumulation factor (BAF), and toxicity value).
Table 3. Scoring system for prioritisation of the quantified substances with the risk relevant parameters (detection frequency, biodegradability, bioaccumulation factor (BAF), and toxicity value).
Detection FrequencyBiodegradability *Log BAF *EC50 (mg/kg)Score
<5%Days<2>10000
5~30%Weeks2~3100~100025
30~55%Weeks–Months3~410~10050
55~80%Months4~51~1075
>80%Recalcitrant>5<1100
* Biodegradability and BAF were estimated using EPI Suite software (United States Environmental Protection Agency, US EPA).
Table
Table 4. Parameters used for the prioritisation of the tentatively identified compounds.
Table 4. Parameters used for the prioritisation of the tentatively identified compounds.
ToxinCAS No.Frequency
%		Log KowBiodegradation Frame *Log BAF *LD50 (Mouse) mg/KgEffectsRef.Smileys
Phytotoxins
Acetoxytropane3423-26-5711.5Week–Months11830Diarrhoea and hypoactivity after administration of 50 and 200 mg/kg[32]CC(=O)OC12CCCC(N1C)CC2
Aconosine38839-95-1171.2Months0.50.27 [33]CCN1CC2CCC(C34C2CC(C31)C5(CC(C6CC4C5C6O)OC)O)OC
Anethole104-46-1132.7Weeks2.312090Lethal oral toxicity in rats at 2 g/kg[34]CC=CC1=CC=C(C=C1)OC
Alantolactone546-43-0293.47Week–Months2.061200Carcinogenic/anticarcinogenic potential; Cytotoxic in vitro[35]CC1CCCC2(C1=CC3C(C2)OC(=O)C3=C)C
Ambrosin509-93-3171,03Week–Months0.21 NF-κβ inhibitor[36,37]CC1CCC2C(C3(C1C=CC3=O)C)OC(=O)C2=C
Apiole523-80-8382.7Week–Months2.214200Acute oral LD50 in rats 3.96 g/kg, in mice 1.52 g/kg; acute dermal LD50 in rabbits > 5 g/kg[38]COC1=C2C(=C(C(=C1)CC=C)OC)OCO2
Arabsin38412-44-1130.76Weeks−0.02 [39]CC1C2CCC3(C(CC(=O)C(C3C2OC1=O)C)O)C
Artemisic acid80286-58-443.8Week–Months4.3950Cytotoxicity[40]CC1CCC(C2C1CCC(=C2)C)C(=C)C(=O)O
Aspidinol519-40-4132.6Week–Months1.0150anti-MRSA activity, with antibacterial effect. Inhibition of the formation of the ribosome[41]CCCC(=O)C1=C(C(=C(C=C1O)OC)C)O
Aspidospermine466-49-9133.78Recalcitrant1.7646.3Cytotoxicity against mouse NIH3T3 cells[42]CCC12CCCN3C1C4(CC3)C(CC2)N(C5=C4C=CC=C5OC)C(=O)C
Bisabolol oxide B26184-88-3212.5Months2.63633Skin reaction; hepatic toxicity[43]CC1=CCC(CC1)C2(CCC(O2)C(C)(C)O)C
Buddledin B62346-21-8132.9Week–Months2.97 Piscicidal activity[44]CC1=CCCC(=C)C2CC(C2C(C1=O)O)(C)C
Conhydrine495-20-5501.21Months0.3911Activation and then blocking of nicotinic acetylcholine receptors[45]CN1CCC23C4C1CC5=C2C(=C(C=C5)OC)OC3C(CC4)O
Cuscohygrine454-14-8291Months0111Autonomic nervous system blockade[46]CN1CCC[C@@H]1CC(=O)C[C@@H]2CCCN2C
Herniarin531-59-9291.74Weeks0.724300Inhibition of human carbonic anhydrase with a concentration of 2.4 µM[47]COC1=CC2=C(C=C1)C=CC(=O)O2
Hygrine496-49-1290.5Week–Months−0.0291 [48]CC(=O)C[C@H]1CCCN1C
Hypoglycine A156-56-933-2.5Day–Weeks−0.0598Jamaican vomiting sickness; hypoglycaemia and death; encephalopathy[49]C=C1CC1CC(C(=O)O)N
Laudanosine2688-77-9253.7Months1.59410GABA receptors interaction glycine receptors, involved in epilepsy and other types of seizures[50]CN1CCC2=CC(=C(C=C2C1CC3=CC(=C(C=C3)OC)OC)OC)OC
Lupanine550-90-3381.6Week–Months0.65410Tremor, Muscle contraction and dyspnoea within mouse[51]C1CCN2CC3CC(C2C1)CN4C3CCCC4=O
Methyl-Jasmonate1211-29-6252.76Weeks1.255000Anti-inflammatory activity in LPS-stimulation within mouse[52]CCC=CCC1C(CCC1=O)CC(=O)OC
Methylpelletierine40199-45-9170.8Week–Months0.0540Taenicide[53,54]CC(=O)CC1CCCCN1C
Methylpseudoconhydrine140-55-6461.5Week–Months0.33250Antinociceptive[55]CC(C(C1=CC=CC=C1)O)N(C)C
Norpseudopelletierine4390-39-0170.2Weeks0.15 Causes severe skin burns and eye damage; genotoxic in vitro + in vivo[56]C1CC2CC(=O)CC(C1)N2
p-Coumaric acid7400-08-081.46Day–Weeks1.811.2Reproductive toxicity[57]C1=CC(=CC=C1C=CC(=O)O)O
Ptaquilosin B87625-62-533NDMonths0.42 Generation of carcinogenic ADN adducts[35]CC1CC2(C=C(C3(CC3)C(C2C1=O)(C)O)C)O
Reticuline485-19-803Months0.6156Ptosis, somnolence, convulsions.[36]CN1CCC2=CC(=C(C=C2C1CC3=CC(=C(C=C3)OC)O)O)OC
Retronecine480-85-371-0.56Weeks−0.04634Carcinogenic, pulmonary oedema, blood lymphoma, convulsions[38]C1CN2CC=C(C2C1O)CO
Swainsonine72741-87-817-1.3Weeks−0.050.35Locoweed intoxication; It is a potent inhibitor of Golgi alpha-mannosidase II[58]C1CC(C2C(C(CN2C1)O)O)O
Tetrahydro-cannabivarin31262-37-0215.76Months3.063Neurotoxicity[59]CCCC1=CC(=C2C3C=C(CCC3C(OC2=C1)(C)C)C)O
Tetraneurin A22621-72-3290.6Week–Months−0.0442Antiviral activity; Ear thickness in rats; dermatitis[60]CC(=O)OCC1CCC2C(C3(C1(CCC3=O)O)C)OC(=O)C2=C
Trachelanthamine14140-18-201.4Week–Months0.691500Somnolence, tremor, muscle weakness[61]CC(C)C(C(C)O)(C(=O)OCC1CCN2C1CCC2)O
Tussilagine80151-77-580.6Week–Months−0.0428.8Carcinogenic in vivo[43,62]CC1(CN2CCCC2C1C(=O)OC)O
Umbelliferone93-35-6211,58Weeks0.410000Inhibition of human carbonic anhydrase 9 catalytic domain[63]C1=CC(=CC2=C1C=CC(=O)O2)O
Xanthotoxol2009-24-7291.16Weeks0.22480Inhibitors of Secretory Acid Sphingomyelinase (S-ASM);[64]C1=CC(=O)OC2=C(C3=C(C=CO3)C=C21)O
Mycotoxins
Aflatoxin B11162-65-8131.45Week–Months0.13.2Carcinogenic, terathogenic[65]COC1=C2C3=C(C(=O)CC3)C(=O)OC2=C4C5C=COC5OC4=C1
Aflatoxin B27220-81-7250.855Week–Months0.18100Carcinogenic, terathogenic; hepatotoxic[66]COC1=C2C3=C(C(=O)CC3)C(=O)OC2=C4C5CCOC5OC4=C1
Alpha-Zearalenol36455-72-8294Weeks1.410.010Chronic toxicity and carcinogenic[67]CC1CCCC(CCCC=CC2=C(C(=CC(=C2)O)O)C(=O)O1)O
Aspergillic acid2152-59-2131.7Week–Months0.8100Antibiotic substance; animal toxicity[49,68]CCC(C)C1=CN=C(C(=O)N1O)CC(C)C
Averufin14016-29-6173Months1.0920.64Inhibition of deaminase[69]CC12CCCC(O1)C3=C(O2)C=C4C(=C3O)C(=O)C5=C(C4=O)C=C(C=C5O)O
Kojic Acid501-30-48-0,64Weeks−0.0523.8Inhibition of human recombinant DAAO[70]C1=C(OC=C(C1=O)O)CO
Azelaic acid19619-43-3131.55Day–Weeks0.645Irritant[71]C(CCCC(=O)O)CCCC(=O)O
Barnol2151-18-002.26Week–Months0.79 [56,62]CCC1=C(C(=C(C(=C1C)O)O)O)C
Cyanotoxins
Anatoxin-a64285-06-9170.8Weeks0.36420Neurotoxicity; muscular fasciculation, respiratory paralysis.[72]CC(=O)C1=CCCC2CCC1N2
MC-LR101043-37-28-1.2Recalcitrant−0.015Hepatotoxicity; visual disturbance, respiratory irritation; vomiting, and muscle weakness[73]CC1C(NC(=O)C(NC(=O)C(C(NC(=O)C(NC(=O)C(NC(=O)C(=C)N(C(=O)CCC(NC1=O)
C(=O)O)C)C)CC(C)C)C(=O)O)C)CCCN=C(N)N)C=CC(=CC(C)C(CC2=CC=CC=C2)OC)C
MC-LW157622-02-185.2Recalcitrant0.810.25-0.33Hepatotoxicity; visual disturbance, respiratory irritation; vomiting, and muscle weakness[74]CC1C(NC(=O)C(NC(=O)C(C(NC(=O)C(NC(=O)C(NC(=O)C(=C)N(C(=O)CCC(NC1=O)C(=O)
O)C)C)CC(C)C)C(=O)O)C)CC2=CNC3=CC=CC=C32)C=CC(=CC(C)C(CC4=CC=CC=C4)OC)C
MC-YR101064-48-68-0.2Recalcitrant−0.0240Hepatotoxicity; visual disturbance, respiratory irritation; vomiting, and muscle weakness[75]CC1C(NC(=O)C(NC(=O)C(C(NC(=O)C(NC(=O)C(NC(=O)C(=C)N(C(=O)CCC(NC1=O)C
(=O)O)C)C)CC2=CC=C(C=C2)O)C(=O)O)C)CCCN=C(N)N)C=CC(=CC(C)C(CC3=CC=CC=C3)OC)C
Nodularin118399-22-741.7Months−0.040.060Hepatotoxicity; visual disturbance, respiratory irritation; vomiting, and muscle weakness[76]CC=C1C(=O)NC(C(C(=O)NC(C(=O)NC(C(C(=O)NC(CCC(=O)N1C)C(=O)O)C)C=CC(=CC(C)C
(CC2=CC=CC=C2)OC)C)CCCN=C(N)N)C)C(=O)O
* Biodegradability and BAF were estimated using EPI Suite software (United States Environmental Protection Agency, US EPA).
Table
Table 5. Prioritisation for ranking the substances detected in the Ter River.
Table 5. Prioritisation for ranking the substances detected in the Ter River.
RankingTentatively Identified Substance
325Tetrahydrocannabivarin
325MC-LW
300Aconosine
300MC-LR
275MC-YR
275Nodularin
250Aflatoxin B1
250Alpha-Zearalenol
225Ptaquilosin B
225Retronecine
225Tussilagine
225Aflatoxin B2
200Aspidospermine
175Artemisic acid
175Conhydrine
175Anatoxin-a
150Bisabolol oxide B
150Swainsonine
150Averufin
125Acetoxytropane
125Apiole
125Aspidinol
125Cuscohygrine
125Hygrine
125Laudanosine
125Lupanine
125Methylpelletierine
125Methylpseudoconhydrine
125Reticuline
125Tetraneurin A
125Aspergillic acid
100Alantolactone
100Buddledin B
100Hypoglycine A
100p-Coumaric acid
100Kojic Acid
100Azelaic acid
75Anethole
75Ambrosin
75Xanthotoxol
50Arabsin
50Herniarin
50Methyl-Jasmonate
50Norpseudopelletierine
50Trachelanthamine
50Umbelliferone
50Barnol
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Picardo, M.; Núñez, O.; Farré, M. Suspect and Target Screening of Natural Toxins in the Ter River Catchment Area in NE Spain and Prioritisation by Their Toxicity. Toxins 2020, 12, 752. https://doi.org/10.3390/toxins12120752

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Picardo M, Núñez O, Farré M. Suspect and Target Screening of Natural Toxins in the Ter River Catchment Area in NE Spain and Prioritisation by Their Toxicity. Toxins. 2020; 12(12):752. https://doi.org/10.3390/toxins12120752

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Picardo, Massimo, Oscar Núñez, and Marinella Farré. 2020. "Suspect and Target Screening of Natural Toxins in the Ter River Catchment Area in NE Spain and Prioritisation by Their Toxicity" Toxins 12, no. 12: 752. https://doi.org/10.3390/toxins12120752

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