Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir

doi:10.21203/rs.3.rs-3961474/v1

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Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir

https://doi.org/10.21203/rs.3.rs-3961474/v1

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Cyanobacterial harmful algal blooms (CyanoHABs) can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. Nevertheless, the potential danger associated with numerous metabolites produced by cyanobacteria remains undisclosed. Only select classes of cyanopeptides have been extensively studied to yield substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co-occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. This research utilized LC-MS-based metabolomics to ascertain the chemical profile of environmental cyanobacterial biomass collected from a eutrophic reservoir in Brazil. Using NP Analyst and Data Fusion-based Discovery (DAFdiscovery) platforms, we examined the correlation between the various cyanopeptides identified in the cyanobacterial crude extract, fractions, and the acute toxicity of these samples against the Artemia. salina microcrustacean. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class, in some cases even higher than those attributed to microcystins. These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. This work also demonstrates the feasibility of conducting risk assessments using a more efficient approach involving LC-MS/MS-based metabolomics and chemometric techniques that require less time and reagents.

cyanotoxins

cyanopeptolins

risk assessment

metabolomics

bioinformatics

bioactivity correlation

Cyanobacterial harmful algal blooms (CyanoHAB) have been a significant problem in freshwater ecosystems. CyanoHABs events, affect water quality, cause ecosystem degradation, and can produce a range of biologically active compounds, which can have adverse effects on both the environment and human health (Huisman et al., 2018; Beversdorf et al., 2017; Huang and Zimba, 2019; Ujvárosi et al., 2020). Cyanobacterial metabolites include lipopolysaccharides, oligopeptides, and alkaloids, many classified as hepatotoxins or neurotoxins. (Buratti et al. 2017; Pearson et al. 2016; Stewart et al. 2018; Violi et al. 2019). Numerous cases of poisoning involving cyanotoxins have been reported (Azevedo et al., 2002; Carmichael et al., 2001; Hilborn et al., 2014; Merel et al., 2013). Among the cyanotoxins, the microcystin (MC) group is undoubtedly the most extensively documented in the literature (Chorus e Welker 2021). MC are a group of cyclic heptapeptides with more than 300 structurally similar congeners (Jones et al., 2020) and the most commonly cyanotoxin class found in cyanobacterial blooms in freshwater systems (De Figueiredo et al., 2004; O'neil et al., 2012). Its ubiquitous presence and proven toxicity led the World Health Organization (WHO) to suggest monitoring cyanobacteria and the MC-LR congener in water reservoirs for human consumption. In response to the toxicological hazards posed by cyanotoxins, numerous countries including Australia, Brazil, Canada, China, Japan, South Africa, USA and the EU have established limit values for MC in drinking water; these measures are implemented to safeguard the population from potential exposure (Picardo et al., 2019).

However, recent advances in analytical techniques have allowed researchers to identify numerous cyanopeptides beyond the well-known class of MC, including, but not limited to, cyanopeptolins, anabaenopeptins, aerucyclamides, aeruginosins, and microginins (Welker and von Döhren, 2006). Over the past decades, hundreds of cyanopeptides from different classes have been identified in biomasses from CyanoHABs events (Beversdorf et al., 2017; Welker et al., 2004; Welker and Von Döhren, 2006). Nevertheless, the occurrence, distribution, and toxicity of many of these bioactive metabolites, especially in tropical freshwater systems, have been poorly explored (Jones et al., 2020). Information about the co-occurrence of different classes of cyanopeptides, exposure concentrations in surface waters, and toxic and synergic effects are lacking. Researchers state that it is paramount to question the diversity of biologically active and potentially toxic cyanopeptides to which humans and wild animals can be exposed during CyanoHABs (Jansen et al., 2019). Some ecotoxicological studies already suggested that the effects of cyanobacterial extracts cannot be attributed solely to MC, indicating that other bioactive metabolites should be studied and considered when assessing risk (Keil et al., 2002; Baumann and Jüttner, 2008; Le Manach et al., 2016; Smutná et al., 2014).

In a time- and reagent-consuming process, traditional ecotoxicological approach assays are typically performed through a series of fractionations that gradually isolate and purify compounds for bioactivity testing (Weller, 2012). However, advances in 'omics' technologies allow researchers to use bioinformatics tools to identify bioactive compounds in the early phases of natural product studies, thus avoiding the exhaustive laborious fractionation steps that can lead to redundant results (Lautié et al., 2020). These new approaches address critical challenges in a comprehensive risk assessment of cyanopeptides, such as the lack of reference standards that address their structural diversity and the large amount of sample required for quantification and identification (Janssen et al., 2019).

Considering this, the present study employs LC-MS-based metabolomic methods associated with chemometric tools in an innovative approach to evaluate the toxicity of cyanopeptides in a cyanobacteria extract from natural biomass collected in a eutrophic reservoir in southeastern Brazil. We applied two chemometric tools (NP Analyst and Data Fusion-based Discovery – DAF discovery) to investigate the correlation between LC-MS/MS data and observed acute toxicity tests. We hypothesized that merging data from untarget metabolomic analyses with toxicological assays could be applied to evaluate the toxicity of cyanopeptides contained in cyanobacteria biomass more quickly and efficiently than traditional ecotoxicological methods. These methodologies can make the environmental monitoring process more agile and with less reagent consumption, accelerating risk assessment and decision-making.

Sampling

Water sample was collected during the dry season (August 2019) at Funil reservoir, a eutrophic system located in the state of Rio de Janeiro, southeastern Brazil (22º30'S and 44º45'W), altitude 440 m, in a humid tropical climate (Cwa in the Köppen system). At that moment, a thick scum accumulated near the shore. The biomass was collected with a plankton net (25 µm mesh), totaling five liters of biomass. The collected material was stored in a plastic bottle and refrigerated during transport. Samples were kept at -20ºC until processing. The pH and temperature of the sample were measured at the time of collection.

Biomass extraction and clean-up

Cyanobacterial biomass was lyophilized and extracted according to the sequential extraction method reported by the United States National Cancer Institute (NCI) for high-throughput extraction and screening of natural products from plants, microorganisms, and marine organisms (McCloud, 2010). Briefly, a mixture of dichloromethane: methanol (DCM: MeOH) (1:1) was added to the lyophilized biomass (265 mg), sonicated for 2 minutes in an ultrasonic bath, and left in contact overnight. The supernatant was collected and stored in a glass vial. The remaining biomass was extracted one more time with MeOH (100%) for eight hours. The supernatant was collected and added to the previous one to form the organic crude extract. The sample was dried under nitrogen flow Crude extracts underwent a solid phase extraction (SPE) clean-up using C18 cartridges (3 mL/500 mg, Chromabond©) previously the LC-MS/MS analysis.

Fractionation

An aliquot of the crude extracts was fractionated using a chromatography column containing Diaion© HP-20SS resin as a solid phase. A step gradient of isopropyl alcohol: H₂O (0:100, 20:80, 40:60, 70:30, 90:10, and 100:0), was applied to obtain fractions F1, F2, F3, F4, F5, and F6, respectively.

LC-MS Analysis

LC-MS analyses were carried out on a high-performance liquid chromatography system (Shimadzu© Prominence Liquid Chromatography) coupled with a high-resolution tandem mass spectrometer (Micro TOF-QII; Bruker Daltonics©, MA, USA) with electrospray ionization (ESI) – (HPLC-ESI-QTOF-MS/MS). Chromatographic separations were performed over a Luna Kinetex C18 column (50 x 2.1 mm, 2.6 µm particle diameter). Mobile phases comprised of 0.2% v/v formic acid in ultrapure water (solvent A) and 0.2% v/v formic acid in acetonitrile (solvent B). The gradient was as follows: A: B 95: 5 (0 min), A: B 0: 100 (in 12 min), A: B 0: 100 (1 min), A: B 95: 5 (in 0.1 min), A: B 95: 5 (until 15 min). A flow rate of 0.6 mL.min^− 1 was applied. The ESI conditions were capillary potential at 3.5 kV, drying gas (N₂) at 200ºC with a flow rate of 9.0 mL.min^− 1, and nebulization pressure of 40.0 psi. The mass spectrometer (QTOF) operated in auto scan MS/MS mode, and the mass spectra were acquired in positive mode with two mass ranges, 50-1500 m/z and 800–2000 m/z (MS1), and in negative mode with a single mass range, 150–2000 m/z (MS1).

LC-MS/MS-based Metabolomics

LC-MS/MS data were converted to .mzXML format with DataAnalysis© software and pre-filtered in MSConvert©. The data were entered into the GNPS© platform (https://gnps.ucsd.edu/) to generate the molecular network using the Classical Molecular Networking tool. GNPS molecular networking parameters were as follows: precursor ion mass tolerance, 0.02 Da; fragment ion mass tolerance, 0.02 Da; min pairs cos, 0.65; network top, 10; minimum matched fragment ions, 4; minimum cluster size: 2; G6 filter as blanks before networking. MS2 spectra of known compounds were also uploaded and “seeded” into the molecular network. They included: aeruginosin NAL2 m/z 587.3571 [M + H]⁺; guanitoxin m/z 253.1051 [M + H]⁺; cyanopeptolin 1020 m/z 1021.5368 [M + H]⁺; cyanostatin B m/z 754.4426 [M + H]⁺; MC-RR m/z 519.7960 [M + H]⁺; mycosporins-lysine m/z 317.1368 [M + H]⁺; microginin KR787 m/z 788.4016 [M + H]⁺; namalide B m/z 576.3391 [M + H]⁺; namalide C m/z 562.3235 [M + H]⁺; namalide D m/z 560.3561 [M + H]⁺; nodularin-R m/z 825.4490 [M + H]⁺; Dhb5-nodularin m/z 811.4328 [M + H]⁺ porphyra 334 m/z 347.1431 [M + H]⁺; shizopeptin 791 m/z 792.4802 [M + H]⁺; shinorine m/z 333.128 [M + H]⁺, and spumigin 638 m/z 639.3109 [M + H]⁺. Cytoscape© software was used to visualize molecular networks. The spectra were automatically scanned for matching patterns in the GNPS spectral libraries (thresholds fixed for cosine score > 0.65 and at least four matched peaks). Cluster annotation in the networks was attempted by cross-referencing MS and MS/MS spectra with the seeds MS2 datasets. Also, the SIRIUS© and ChemCalc© platforms were used to search for prospective molecular formulas. The search for known compounds was performed using the databases PubChem©, Chemspider©, Metlin©, NPAtlas©, Dictionary of Natural Products©, GNPS, and CyanoMetDB (JONES et al., 2020).

Acute toxicity assay against Artemia salina

Artemia salina cysts were hatched for 24 h in artificial seawater under constant aeration and lighting at an average temperature of 25°C. The motile nauplii were transferred, along with seawater, to 96-well plates. Biological assays were performed in triplicate, using potassium dichromate as a positive control and 1% dimethyl sulfoxide (DMSO) as a negative control. A blank test was performed with an artificial seawater solution. The crude extract samples were dissolved in DMSO by serial dilution (100 mg.mL^− 1, 50 mg.mL^− 1, 10 mg.mL^− 1, 5 mg.mL^− 1, 1 mg.mL^− 1, 0.1 mg.mL^− 1, and 0.01 mg.mL^− 1). Fraction samples were prepared at a single concentration of 100 mg.mL^− 1.

To each well were added 99 µL of artificial seawater solution containing 10 ~ 20 nauplii of A. salina and 1 µL of sample or control solution (final sample concentrations: 1000, 500, 100, 50, 10, 1, and 0.1 µg.mL^− 1; final concentration of negative control: 1%; final concentration of positive control: 200 µg.mL^− 1). Plates were incubated at 25°C in the dark for 24h.Dead nauplii were counted using a colony counter, and mortality was calculated as the ratio of dead nauplii to the total number of nauplii in the respective well. The LC₅₀ was calculated by nonlinear regression, and the dose-response function was calculated using the software Origin© 2019.

Integration of MS-based metabolomics data and biological assay results

NP Analyst (https://www.npanalyst.org/) and Data Fusion-based Discovery (DAFdiscovery) were employed to integrate MS-based metabolomics data and bioactivity assay results. NP Analyst is an open online data integration platform for predicting compound biological activities from complex mixtures; providing insights into bioactive constituents and their modes of action by comparing the distribution of mass spectrometry characteristics across the entire sample set with biological signatures obtained from each sample through biological assays (Lee et al., 2022) DAFdiscovery is a notebook-based application that allows users to combine MS, NMR, and bioactivity data, or any combination of two, enabling data fusion and search of interest compounds through correlation calculations (Borges et al., 2022). This innovative platform integrates MS metabolomics data with a suite of bioassays (e.g., % inhibition, IC50, LC50, etc), employing advanced techniques such as Statistical Total Correlation (STOCSY) and Statistical Heterospectroscopy (SHY) for comprehensive analyses (Borges et al., 2022).

The fractions' MS data generated by the Classic Molecular Networking GNPS pipeline (same parameters aforementioned) were uploaded to the NP Analyst platform as a .graphml file. NP Analyst results were displayed as Activity Scores and Cluster Scores (a measure of the consistency of the activity profile). The Activity Score quantifies the strength of a phenotype associated with a specific mass spectrometry feature. This score is computed by summing the mean of the squares of bioactivity values across all extracts containing the feature (range from 0–1). The Cluster Score assesses the coherence of biological fingerprints across all samples harboring a particular mass spectrometry feature. This score is calculated by averaging the Pearson similarity scores between the biological fingerprints of these extracts (ranging from − 1 to + 1) (Lee et al., 2022).

For the DAFdiscovery platform, MS data and bioactivity results were uploaded as .csv files, pipeline option 4 (fusion of MS and bioassay data). The output was a scatter plot (retention time vs. m/z) that shows the correlation (bioactivity correlation: 1 to 1) between the MS profile and the bioactivity readouts.

Biomass characterization

The total cyanobacterial biomass in the sample was composed of 71.75% Dolichospermum circinalis (Rabenhorst) Wacklin, Hoffmann & Komárek, 23.43% Microcystis aeruginosa (Kützing) Kützing, 3.27% Dolichospermum spiroidis (Klebhan) Wacklin, L. Hoffmann & Komárek, and 1.21% Microcystis panniformis Komárek et al.

Dereplication and annotation

MS/MS data from extracts and standard samples were analyzed using the Classic GNPS Molecular Network tool to generate a molecular network. This molecular network presented 464 nodes, 11 exclusives to the standard "seeds" analyzed with the extract samples. MS data were also analyzed by the GNPS tools DEREPLICATOR + and the Network Annotation Propagation (NAP) to help with features' dereplication. Additionally, we manually checked for the features' exact masses, isotope patterns, and fragmentation patterns during the dereplication process. The combination of these tools revealed the presence of cyanopeptides of four different classes: microcystins (MC), microginins (MG), aeruginosins (AER), and cyanopeptolines (CP).

The MC cluster (Fig. 1A) revealed the presence of the microcystin variants MC-LR (m/z 995.5508 [M + H]⁺ and m/z 498.2807 [M + 2H]⁺), MC-YL (m/z 1002.5105 [M + H]⁺), MC-YR (m/z 1045.5435 [M + H]⁺), [D-Asp] MC-YR (m/z 1031.5229 [M + H]⁺), MC-WR (m/z 1068.5594 [M + H]+), [D-Asp3] MC-Hphr (m/z 1029.5434, MC-RR (m/z 519.7880 [M + 2H]⁺), and [D-Asp3] MC-RR (m/z 512.7806 [M + 2H]⁺ ).

The dereplication of the MG cluster (Fig. 1B) revealed the presence of cyanostatin B (m/z 754.4426 [M + H]⁺), microginin MG KR787 (m/z 788.4016 [M + H]⁺), and microginin MG KR604 (m/z 605.3879 [M + H]⁺). The proposition of the molecular formula C₄₁H₆₁N₅O₉ (error 5.40 ppm) for the feature of m/z 768.4589 [M + H]⁺ was based on the mass difference of 14.0163 Da between that feature and cyanostatin B, suggesting that its molecular formula an additional -CH₂ group. This molecular formula was not found in databases, indicating the possibility of an unprecedented variant of the microginins class.

In the CP cluster (Fig. 1C), compounds CP 986 and CP 972 were annotated according to their spectral similarity to the standard seeds. Based on the mass difference of 14.0224 Da between CP 986 and the feature of m/z 1001.5777 [M + H]⁺, we proposed the molecular formula C₄₈H₇₆N₁₀O₁₃ (Δ error − 1.70 ppm). Based on the mass difference of 85.0476 Da between CP 986 and m/z 1072.6029 [M + H]⁺, the molecular formula C₅₁H₈₁N₁₁O₁₄ (Δ error − 1.21 ppm) was proposed. Also, considering the difference of 28.0324 Da between compounds m/z 1072.6029 [M + H]⁺ and m/z 1044.5702 [M + H]⁺, the formula C₄₉H₇₇N₁₁O₁₄ (Δ error − 2,65 ppm) was suggested. The SIRIUS© tool supported this annotation and suggested the same molecular formulas for these compounds based on MS1 and MS2 data. The tool classified the first one as a depsipeptide, the second as a peptide, and the last as a cyclic depsipeptide. A search in the literature did not show studies that addressed these cyanopeptolins, indicating that they may be new analogs of this class.

In the AER cluster (Fig. 1D), aeruginosin 298A (m/z 605.3661 [M + H]⁺) was annotated based on spectral similarity to the standard. Based on the mass difference (83.9835 Da) between AER NAL2 (standard seed - C₃₀H₄₆N₆O₆) and the feature of m/z 671.3392 [M + H]⁺, we proposed the molecular formula C₃₃H₄₆N₆O₉ (Δ error − 1.86 ppm). Based on the mass difference of 15.9968 Da between this last compound and the compound with m/z 655.3424 [M + H]⁺, we propose the molecular formula C₃₃H₄₆N₆O₈ (Δ error − 4.79 ppm). The SIRIUS© tool suggested the same molecular formulas for these features; but did not suggest a chemical class for them.

Analysis of acute toxicity against the microcrustacean Artemia salina

Acute toxicity tests with A. salina showed more than 50% mortality of nauplii at concentrations ≥ 500 µg.mL^− 1 of crude extract (Fig. 2); resulting in an LC50 = 278 µg.mL^− 1 (± 33.7 µg.mL-1) (Nguta, 2011). Regarding the fractions, F2 and F3 were those that showed significant toxicity, causing 54% and 74% of nauplii mortality, respectively (Fig. 3).

Prediction of compound biological activities

NP Analyst results (Job ID: e40a0786-bb6e-4274-992d-4c82665b37a8) attributed activity scores to the fractions’ features that ranged from 0.04 to 0.54. Cluster scores were considered between 0.1 and 1. The activity scores varied widely between the different classes of cyanopeptides. However, within the same cyanopeptide class, the activity scores were consistent between the compounds. (Table 1). The MC activity scores range from 0.35 to 0.40. The highest score (0.40) was given to MC-WR m/z 1068.55 [M + H]⁺ and the lowest (0.35) to the variant [D-Asp]³MC-Hphr, both present in the active fractions F2 and F3. The MG and AER compounds received the lowest activity scores (0.10–0.18). The highest activity score (0.54) was attributed to the CP analogs. The scatterplot is shown in Supplementary Material (Fig. S1).

The DAFdiscovery platform attributed bioactivity correlations between 0.31 and 0.99 to the fractions’ features. The scatterplot is shown in Supplementary material (S2). Similar to observed in the NP Analyst analysis, AER compounds received the lowest bioactivity correlation scores, and compounds of the CP class received the highest ones by this platform. Compounds from the MC class also received similar bioactivity correlation scores (0.54–0.58) except for MC-YR (0.44) and MC-RR (0.99). Still, unlike the NP Analyst results, cyanostatin B and MG KR787 received a high bioactivity correlation value, 0.81 and 0.75, respectively.

Table 1

Activity score and bioactivity correlation attributed to different cyanopeptides by the NP Analyst and DAF Discovery platforms.
m/z	Putative compound/class	NP Analyst		DAFdiscovery
m/z	Putative compound/class	Activity Score	Cluster Score	Bioactivity correlation	Fraction
519.7880	MC-RR/microcystin	0.08	0.17	0.99	F2, F3, F4, F5, F6
605.3651	Aeruginosin 298A/aeruginosin	0.18	0.4	0.31	F2, F3, F6
754.4426	Cyanostatin B/microginin	0.1	0.21	0.81	F2, F3, F4, F5
973.5362	CP 972/cyanopeptolin	0.54	1	0.71	F2, F3
987.5553	CP 986/cyanopeptolin	0.54	1	0.76	F2, F3
788.4016	MG KR787/microginin	0.18	0.4	0.75	F2, F3, F4
995.5508	MC-LR/microcystin	0.39	1	0.58	F2, F3
1002.5105	MC-YL/microcystin	0.39	1	0.44	F2, F3
1029.5434	[D-Asp3] MC-HphR/microcystin	0.35	1	0.57	F2, F3
1044.5702	C₄₉H₇₇N₁₁O₁₄*/cyanopeptolin	0.54	1	0.81	F3
1045.5435	MC-YR/microcystin	0.39	1	0.54	F2, F3
1068.5594	MC-WR/ microcystin	0.4	1	0.55	F2, F3
1072.6029	C₅₁H₈₁N₁₁O₁₄*/cyanopeptolin	0.54	1	0.75	F3
*Putative molecular formula

In this study, we applied LC-MS-based metabolomics and bioinformatic tools (DAFDiscovery and NP Analyst) to investigate the toxicity/bioactivity of cyanobacteria crude extract from a tropical and eutrophic reservoir. We hypothesize that the combination of metabolomic methods with bioinformatic tools is efficient for evaluating the toxicity of different cyanopeptides contained in the crude cyanobacteria extract without requiring compound isolation.. Our results revealed the presence of compounds belonging to four cyanopeptides classes, microcystins, microginins, cyanopeptolins, and aeruginosins, in the tested cyanobacterial biomass. All these compound classes consist of biologically active metabolites that can influence the function of cellular enzymes, disrupt cellular signaling pathways, and induce tissue apoptosis, potentially leading to the mortality of organisms (Huang and Zimba, 2019). However, only microcystins figure in the management legislation for freshwater legislation worldwide (Chorus and Welker, 2021). Both chemometric tools used in this work efficiently graded the biological activity of the compounds found in the cyanobacterial biomass, each one with its specific characteristics of the data interpretation method.

Biomass toxicity

In aquatic systems, the cyanobacterial community produces a complex array of metabolites. All these compounds can be released mainly after the cyanobacterial cells’ lysis (Rohrlack and Hyenstrand, 2007), affecting aquatic organisms (Pearson et al., 2016; Toporowska et al., 2014; Ferrão-Filho and Kozlowsky-Suzuki, 2011; Metcalf and Codd, 2012; Osswald et al., 2007). Although cyanobacterial toxins and secondary metabolites co-occurrence are ecologically relevant (Fernandes et al., 2019), few studies explored the biological activities of crude extracts, especially in tropical systems. In this study, to assess the biomass ecotoxicity, we performed an acute toxicity test with crude extract from collected sample against A. salina. The evaluated biomass was composed predominantly of Dolichospermum spp. (~ 75%) and Microcystis spp. (~ 25%); and we have observed 100% mortality of A. salina nauplii with 500 µg.mL^− 1 of crude extract after 24 hours of exposure, generating a LC₅₀ of 278 mg.L^− 1. According to Nguta et al. (2011) criteria, the crude extract used in this study was categorized as moderately toxic (LC₅₀ = 278 µg.mL^− 1). The LC₅₀ provided by our test is higher than previously reported LC₅₀ values for Microcystis bloom extracts collected from tropical reservoirs in Morocco (ranging from 1 to 46 mg.L^− 1) that used similar evaluation methodology (Douma et al., 2017, 2010; Sabour et al., 2002). Although both Dolichospermum spp. and Microcystis spp. are known producers of microcystins (Chorus, 2001; Dreher et al., 2019), the variation in the results from acute testes can be attributed to the difference in the biomass species composition, the fluctuation of toxic clones in the samples, and the specific environmental conditions from which the biomass originated (Ibelings et al., 2021). For example, a comparison between extracts of Microcystis spp. and Planktothrix agardhii, which had similar total microcystin concentrations, revealed significant differences in the profile of microcystin types and other oligopeptides, leading to varying toxic effects in the organisms tested (Pawlik-Skowrońska et al., 2019). Furthermore, stronger toxic effects were observed by the binary mixture of anabaenopeptin-B with MC-LR at a total concentration equal to the concentration of the compounds when tested individually (Pawlik-Skowrońska & Bownik, 2022). This wide variation in results reinforces the need for toxicity studies with crude extracts from biomass environmental samples. Although there are already several studies investigating the toxicity of cyanopeptides, the biomass used generally comes from cultures of isolated strains, and toxicity tests are mainly performed with extracts from a single strain. The problem with these methodologies is that they ignore the chemo-ecological complexity of the interaction of different compounds. Also, the tested concentrations will hardly be found in natural conditions, distancing the results obtained from environmental reality.

Cyanopeptide profile and Bioactivity Correlation

The bioactivity correlation analysis in this study links different classes of cyanopeptides (MC, MG, CP, and AER) with the toxicity observed in the tests with A. salina. The cyanopeptides group with the highest diversity of congeners identified in our extract were MCs, with seven congeners. This class is the most widely described and includes diverse cyanobacterial toxins documented in the literature (Preece et al., 2017), with nearly 300 known congeners (Jones et al., 2020). All MC variants recorded in this study, except the MC-YL, already have an LC₅₀ described in the literature, ranging from 50 (-LR) to 600 (-RR) µg.kg^− 1 of body weight intraperitoneal injection in mice bioassays (Botes et al., 1985; del Campo and Ouahid, 2010; Kusumi et al., 1987; Meriluoto et al., 1989; Namikoshi et al., 1992; Stoner et al., 1989). For all MC analogs found in our extract, except the MC-RR, results from the NP Analyst platform gave the second-highest activity scores (0.35–0.40). The DAFdiscovery platform results attributed bioactivity correlations to the MC variants ranging from 0.44 (-YL) to 0.99 (-RR). Although MC-RR showed the lowest activity score in the NP Analyst analysis (0.08), the bioactivity correlation in the DAFdiscovery analyses presented the highest value (0.99). This variation is probably due to the different algorithms used by each platform. NP Analyst considers the molecule's presence in the active fraction(s) and its absence in the inactive ones (cluster score). If a molecule is present only in the active fraction, it will have a higher score, and if it also appears in inactive fractions, even at much lower concentrations, it will receive a lower score. The DAFdiscovery platform considers the peak intensity in each fraction. High concentrations in bioactive fractions pull the bioactivity correlation up. In a small number of samples, if a compound has a high concentration in a given bioactive fraction and a low concentration in a non-active fraction, it will still have a high bioactivity correlation.

The MG are linear peptides that vary, with few exceptions, from four to six amino acids and generally present a decanoic acid derivative, Ahda (3-amino-2-hydroxy-decanoic acid), at the N-terminus (Welker and Von Döhren, 2006). Approximately 40 congeners are known, and this number is still increasing (Lodin-Friedman and Carmeli, 2018; Riba et al., 2019; Ujvárosi et al., 2020). In this work, we found the presence of cyanostatin B (m/z 754.4426 [M + H]⁺), microginin MG KR787 (m/z 788.4016 [M + H]⁺), microginin MG KR604 (m/z 605.3879 [M + H]⁺). We proposed the molecular formula C₄₁H₆₁N₅O₉ to the feature m/z 768.4589 [M + H]⁺ that may be a potentially new MG congener.

MG analogs have been named as microginines, nostoginins, oscillaginins and cyanostatins, according to their producing species (Bagchi et al., 2016; Ujvárosi et al., 2020). These compounds are inhibitors of zinc metalloprotease and are known to be inhibitors of aminopeptidase M (APM), leucine aminopeptidase (LAP) and angiotensin-converting enzyme (ACE) (Ishida et al., 2000; Lifshits et al., 2011; Lodin-Friedman and Carmeli, 2018; Sano et al., 2005). The mechanism of enzyme inhibition is mainly attributed to the Ahda portion, although NeMe-Tyr-Tyr at the C terminus favors binding to the active site of the enzymes (Ishida et al., 2000). Data on the toxic activity of MG on animal models are scarce. Ujvárosi et al. (2020) determined in vitro cytotoxicity and genotoxicity of four MG congeners (cyanostatin B, MG FR3, MG GH787, MGL 402) and a well-characterized cyanobacterial extract encompassing these metabolites. In that study, the extract significantly affected the viability of cells in the MTT assay with the human hepatocellular carcinoma cell line (HepG2). Also, the extract and all tested congeners induced DNA strand breaks. Fernandes et al. (2019) tested a crude extract of Microcystis sp. containing several MG. They demonstrated its acute toxicity in larvae of a tropical freshwater fish Astyanax altiparanae, reporting numerous abnormalities, including abdominal and pericardial edema. In our study, the DAFdiscovery shows a high bioactivity correlation (0.75–0.81) for MG compounds. However, a low activity score (0.1–0.18) was assigned by NP Analyst. This difference is likely due to the different parameters considered by each algorithm. For example, MG KR787 appears in higher concentration in the active fraction F3 which gives it a high correlation of activity in DAFdiscovery. However, it is also present, although at a lower concentration, in the non-active fraction F4, which makes it assigned a low activity score by the NP Analyst. The scores assigned to the cyanostatin B variant followed the same reasoning.

The CP are a broad group of cyclic depsipeptides that can be produced by various cyanobacterial genera, including Planktothrix, Anabaena, Nostoc and Microcystis (Welker and Von Döhren, 2006). These molecules comprise a six-amino acid ring and a side chain with one or two residues. All CP are characterized by 3-amino-6-hydroxy-2-piperidone (Ahp) in position 3 (Mazur-Marzec et al., 2018). All positions in the ring, except Thr and Ahp, can be occupied by variable amino acids, giving the CP a wide structural variability (Welker and Von Döhren, 2006).

Certain CP have demonstrated inhibitory effects on serine proteases, including elastase, chymotrypsin, thrombin, and trypsin (Gesner-Apter and Carmeli, 2009; Linington et al., 2008; Weckesser et al., 1996). In our study, both platforms attributed to CP compounds the highest bioactivity correlations (0.54 by NP Analyst and 0.71–0.81 by DAFdiscovery), including the potentially novel analogs (C₄₉H₇₇N₁₁O₁₄ and C₅₁H₈₁N₁₁O₁₄). These results provide compelling evidence supporting the hypothesis that CP are indeed one of the classes that should be considered among the monitoring cyanotoxins. Furthermore, these findings illustrate the importance of environmental monitoring for discovering new cyanopeptides congeners. Gademann et al. (2010) has already shown that CP 1020, isolated from Microcystis, was toxic (LC₅₀ = 8.8 µM) to the freshwater crustacean Thamnocephalus platyurus in similar concentrations to some well-known MC. Additionally, Faltermann et al. (2014) pointed out that exposure of zebrafish embryos to different concentrations of CP 1020 resulted in transcriptional alterations of genes related to several biological pathways.

The AER are a class of linear tetrapeptides with uncommon 2-carboxy-6-hydroxyoctahydroindole (Choi) and (4-hydroxy) phenyl lactic acid (Hpla) moieties and an arginine derivative at the C terminus (Ishida et al., 1999). AER is a cyanopeptide family with some potent inhibitors of serine proteases (Ersmark et al., 2008). The inhibitory mechanisms have been elucidated by X-ray crystallography of aeruginosin-protease complexes (Hicks et al., 2006). In our analysis, the presence of AER 298A was correlated with bioactivity by both platforms (scores 0.18 and 0.31 using NP Analyst and DAFdiscovery, respectively). Scherer et al. (2016) stated that some AER variants match the well-known MC-LR in the toxicity against crustaceans. Their studies reported a LC₅₀ = 34.5 µM for the synthetic AER 828A, a chlorosulfopeptide analogue, against the organism T. platyurus. Similar results were found by Kohler et al. (2014), that reported a LC₅₀ value of 22.4 µM for this AER variant against T. platyurus.

Using chemometric tools to predict the toxicity of compounds in complex mixtures

In this study, we used DAFdiscovery and NP Analyst tools to investigate the bioactivity correlation of different cyanopeptides in a complex extract from a biomass collected in a tropical reservoir. Both tools proved to be effective, each one with its particularities. Bioinformatics tools are suggested as a cost-effective and straightforward way to determine if a compound contributes to toxicity in a complex extract without the need to isolate the compound or use standards for biological tests (Atasanov et al., 2021). The bioactivity correlation results from this study reinforce that different cyanopeptides in the sample contribute to the overall toxicity observed. Based on current literature, MC was expected to exhibit the highest bioactive activity. However, to our surprise, microginins, aeruginosins, and, mainly, cyanopeptolins also strongly correlated with the toxicity observed in our tests. This study also suggested that the biomass extract contained potentially novel cyanopeptolins with high bioactivity correlations. Furthermore, we cannot rule out the synergistic action of these substances, as we are talking about a complex extract and not isolated compounds. Regarding the particularities of the tested tools, DAFdiscovery is suggested when the number of samples is high and when the separation achieved between each extract or fraction is inefficient since it considers the ionized compound's peak intensity in each sample. In turn, NP Analyst works better for a small number of samples and when the separation of compounds occurs efficiently, as the tool tends to smooth the correlation values if a compound appears in non-active fractions. Although this can lead to underestimating the molecule's toxicity, NP Analyst also provides another index, the cluster compound value, that helps interpret the results.

Metabolomics and chemometric tools proved be a promising alternative for identifying cyanopeptides congeners and studying the toxicity/bioactivity in complex extracts and fractions without the need to isolate the compounds in a less time-and-reagent consuming process. Both tools, DAFdiscovery and NP Analyst, used in this study contributed substantially to the toxicity assessment process, and the choice between them will depend on the user's data (e.g. number of samples, efficiency in the pre-fractionation process). We observed that both results could be complementary in the toxicity assessment. The findings presented herein highlight that the toxicity observed in the cyanobacteria biomass can not solely attributed to MC, underscoring the importance of thoroughly evaluating the (eco)toxicological hazards associated with various classes of cyanopeptides.

Ethical Approval

Not applicable

Consent to Participate

Not applicable

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.

Consent to Publish

All authors reviewed the final manuscript and agreed to its publication in the journal Environmental Science and Pollution Research

Funding

Rhuana Valdetario Medice and Renan Silva Arruda have received research support from Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES), Brazil as a student scholarship. Jaewon Yoon reports financial support provided by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) as a scientific initiation fellowship. (2023/00525-8). Camila Manoel Crnkovic has received financial support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) grants numbers 2020/07710-7 and 2022/02872-4. Ricardo Moreira Borges reports that financial support was provided by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), grant number 210.489/2019 APQ-1. Marcelo Manzi Marinho reports financial support was provided by Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES), Brazil, Finacial code 001, Agência Nacional de Águas (ANA), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil, Grant 403515/2016-5; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro, Brazil (FAPERJ); Grant 303572/2017-5,

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study's conception and design. Rhuana Valdetário Médice and Renan Silva Arruda performed material preparation and data collection. Rhuana Valdetário Médice did the LC-MS/MS analysis and wrote the first draft of the manuscript. Renan Silva Arruda and Natalia Pessoa Noyma did the sample collection and the phytoplankton identification. Jaewon Yoon did the Artemia salina assay. Ricardo Moreira Borges and Camila Manoel Crnkovic helped with the data analysis. Miquel Lurling, Marcelo Manzi Marinho, Camila Manoel Crnkovic, and Ernani Pinto commented on previous versions of the manuscript. All authors read and approved the final manuscript. The authors Rhuana Valdetário Médice and Renan Silva Arruda are co-authors of the manuscript.

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Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir

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Abstract

Figures

INTRODUCTION

METHODS

Sampling

Biomass extraction and clean-up

Fractionation

LC-MS Analysis

LC-MS/MS-based Metabolomics

Acute toxicity assay against Artemia salina

Integration of MS-based metabolomics data and biological assay results

RESULTS

Biomass characterization

Dereplication and annotation

Prediction of compound biological activities

DISCUSSION

Biomass toxicity

Cyanopeptide profile and Bioactivity Correlation

Using chemometric tools to predict the toxicity of compounds in complex mixtures

Conclusion

STATEMENTS AND DECLARATIONS

References

Additional Declarations

Supplementary Files

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