Oxidative stress and osmolyte induction caused by a Thiobencarb nano-emulsion in the freshwater alga Chlorella vulgaris
Khaled Y. Abdel-Halim, Soad M. Mohy El-Din, Nadia H. Noaman, Manal M. El-Abasy
TL;DR
This study examines how traditional and nano-forms of a pesticide affect a freshwater alga, showing increased stress and osmolyte production.
Contribution
The study compares the toxic effects of traditional and nano-formulations of Thiobencarb on Chlorella vulgaris using oxidative stress and osmolyte responses.
Findings
Nano-pesticide application significantly reduced protein content in Chlorella vulgaris.
Both traditional and nano-forms of Thiobencarb increased antioxidant enzyme activity in treated cells.
Nano-derived Thiobencarb caused higher sucrose content compared to traditional formulations.
Abstract
Using nano-pesticides significantly affected risk evaluation. The risk evaluation was significantly affected by nano-pesticide use, as determined using the microalga Chlorella vulgaris. Thiobencarb (THIO) traditional and nano-emulsion formulations were applied with sub-lethal dosages to induce oxidative stress and osmolyte production in C. vulgaris. Three concentration levels were tested for 96 h on microalga for each traditional and nano formulation according to their median effective concentration EC50, 0.1 EC50, and 0.025 EC50. Every treatment significantly (P < 0.05) reduced the overall protein content in equivalence to the control (276.92 mg/g). Nonetheless, the treated cells had higher carbohydrate content than the control. Malondialdehyde (MDA) significantly increased (P < 0.05) in the treated cells. The antioxidant enzymes: catalase (CAT), superoxide dismutase (SOD), and…
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Figure 7- —Alexandria University
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Taxonomy
TopicsDiatoms and Algae Research · Marine Biology and Environmental Chemistry · Marine and coastal ecosystems
Introduction
Herbicides play a vital role in controlling weeds and boosting crop production. However, they may also affect non-target organisms such as algae. It is therefore important to investigate their potential impact on aquatic primary producers through ecotoxicological research (Ma et al. 2001). Herbicides also can modify species’ communities of aquatic ecosystems regarding algae, which can have an impact on their structure and function. Some herbicides have an extended half-life, which raises potential problems in water quality, ecosystem safety and human health (Kim et al. 2017). Despite these concerns, there are few investigations on the impact of herbicides on freshwater algae. The active ingredient (a.i.) of the contact herbicide Saturn^®^ is thiobencarb (THIO), which is effective against sedges, fescue, grasses, and broad-leaf weeds. THIO is particularly used for paddy rice fields; it is always applied for pre-emergence and early post-emergence weed management (Abdel-Halim and Massoud 2014).
Algae are an essential component of aquatic ecosystems. They have been identified as biological indicators (bio-indicators) for impacts of industrial pollutants and other materials due to their varied responses to such substances (Fargasova and Kizlink 1996). Due to their rapid production and high adaptability, microalgae are crucial for ecosystem strength. They significantly influence the environment and can affect larger organisms (McCormick and Cairns 1994). Additionally, microalgal assays are often quick, inexpensive, and sensitive (Sosak-Swiderska et al. 1998). As a consequence, microalgal toxicity assessments increased and are currently required by authorities for controlling chemical dispersal in the environment. To determine how microalgae respond to a toxicant, some physiological cellular parameters such as pigments, protein, carbohydrate, fatty acid, osmolyte content, antioxidant enzyme level, and oxidant marker (malondialdehyde, MDA) are often utilised (Mayer et al. 1997). Oxidative damage is caused by the reactive oxygen species (ROS) that herbicides produce. Overexposure to ROS disrupts proteins, DNA, RNA, lipids, and algae pigments (Schützendübel and Polle 2002). Most herbicides primarily interfere with regular photosynthesis, which modifies the production of lipids and carbohydrates (Allen and Forsberg 2001).
Nanotechnology-based agrochemicals benefit from batches of interest due to novelty aims. As innovative products, nanoparticles (NPs) enhance conventional agrochemicals’ efficacy, safety, and environmental friendliness (Ali et al. 2024). Regarding the application of pesticides, nano-pesticides, and herbicides, there is a new technical advancement that may give a variety of benefits such as increasing stability, effectiveness, and the least amount of a.i. An extensive range of formulations have been suggested, including metals, metal oxides, polymers, nano-clays, emulsions (like nano-emulsions), and nano-capsules (like polymers) (Kookana et al. 2014). There are many concerns about nano-pesticides’ effects on non-target organisms, such as algae, and their environmental persistence when compared to conventional formulations.
Few studies are interested in the risks associated with nano-products although some have applied partial protocols to assess their toxicity and environmental fate (Kah et al. 2014; Kookana et al. 2014). Various studies demonstrated that exposure to nano-materials (NMs) induces adverse biological effects on algae, which may further affect algae’s gene expression, metabolism, photosynthesis, nitrogen fixation, and growth (Chen et al. 2019; Huang et al. 2022). Most of these studies indicated the potency of metallic NPs, e.g., titanium dioxide nanoparticles (TiO₂NPs) (Metzler et al. 2011), nano-silicone dioxide (Wei et al. 2010), C₆₀ fullerene (Kubatova et al. 2013), and single-walled carbon nanotubes (SWCNTs) (Sohn et al. 2015). Likewise, there is a lack of concern about the adverse biological effects of nano-pesticides on the algae. Some investigations evaluated adverse impacts of nano-emulsions on algae, e.g., nano-pendimethalin on C. vulgaris (Noaman et al. 2020) and nano-hexaconazole on blue-green algae (Kumar et al. 2016). Before using nano-pesticides, it may be clarified to a largely inadequate extent for accurate risk evaluation. In a fair evaluation, one should consider the advantages and disadvantages of nano-pesticides in comparison to existing options. This could not be achievable, nevertheless, given that the items that have been covered up to this point limit the study of products that are anticipated to be released in the upcoming ten years, suggesting that fair assessment might be feasible (Noaman et al. 2020). Among the most often utilised species in microalgal toxicity testing is C. vulgaris, which is thought to be a reliable indication for determining the answers to these questions Table 1, 2.Table 1. Herbicide types and providersItemHerbicidesTypeProvider1Thiobencarb (Sioun^®^ 50% EC)IUPAC Name: S-[(4-chlorophenyl) methyl] N, N-diethyl carbamothioateLNC Co., Ltd., China^a^Technical (95%)CAPL, ARC, Egypt^b^^a^Lianyungang Neutech Chemical Co., Ltd., China^b^Central Agricultural Pesticides Laboratory (CAPL), Agricultural Research Center (ARC), Giza, EgyptTable 2All chemicals with providers that are utilised in recent investigationsNo.ItemProvider1EthanolJ.T. Baker chemical Co., Philipsburg, N.J. 088652Sodium bicarbonate3Sodium phosphate monobasic4Sodium phosphate dibasic5Potassium phosphate monobasic6Potassium phosphate dibasic7Aqueous KOH8Sulfuric acid9Glacial acetic acid10MethanolBDH laboratory supplies pool, BH 15 1 T, England11Chloroform12n-hexane13Riboflavin 99%Sigma Chemical Co. P.O. box 14508 St. Louis, Mo 63178 USA14Ninhydrin 99%15Nitro blue tetrazolium (NBT; 99.8%)16Thiobarbituric acid (TBA)LOBA CHEMIE put. Ltd, India17Anthrone (C_14_H_10_O)185-sulphosalicylic acid19Trichloroacetic acid (TCA)SDFCL-CHEM Limited, India20AscorbateMerck, Darmstadt, Germany.21Hexahydrate ferric chloride (FeCl_3_. 6H_2_O)22Boric acid (H_3_BO_3_)ADWIC Lab., Egypt23Magnesium sulfate, 7-Hydrate (MgSO₄·7H₂O)24Folin reagentBDH Chemicals Ltd England.25Toluene26Proline standard27Hydrogen peroxide (H_2_O_2_)28Sodium hydroxide (NaOH)29Sodium carbonate (Na_2_CO_3_)30Sodium nitrate (NaNO_3_)31Magnesium chloride hexahydrate (MgCl_2_· 6H_2_O)32Cobalt (II) chloride hexahydrate (CoCl_2_· 6H_2_O)33Potassium sodium tartrate34Copper (II) chloride dihydrate (CuCl_2_. 2H_2_O)35Copper sulfate (CuSO_4_)36Ethylenediamine tetra acetic acid (EDTA)37Calcium chloride dihydrate (CaCl_2_. 2H_2_O)38Manganese (II) chloride tetrahydrate (MnCl_2_· 4H_2_O)
Material and methods
Herbicides and chemicals
Herbicides
Herbicides were provided as declared in the following table:
Chemicals
All utilised chemicals (with providers) in the current investigation are listed in the following table.
Preparation of nano-emulsion and characterization
According to Gupta et al. (2016), a nano-emulsion of THIO 50% was prepared under the high-energy mode. The technical material (95%) was liquefied in a vegetable-based oil and used to diffuse into water with surfactant and co-surfactant, creating a homogeneous solution. Depending on ICH guidelines Q1A (ICH 2003), the nano-derived version of THIO was evaluated for emulsion stability across ranges of temperature and humidity storage conditions. Then, characterization of this version was detected by transmission electron microscopy (TEM) (JOEL 1400 Plus, Japan) and Fourier Transform Infrared (FTIR) (TENSOR 27 Buker, Germany-FTIR L203/12887), as previously stated by Abdel-Halim et al (2021).
Cultivation conditions and experimental design
The organism under test, C. vulgaris was procured as a “starter” culture brought from the Institute of Oceanography and Fisheries located in Alexandria, Egypt. It was grown in Allen and Arnon’s medium (AAM) (Miller et al. 1978). The nutrient medium was prepared by adding 1 ml of each stock solution to one litre of distilled water. The final pH was adjusted to 7.5 using 0.1 N NaOH or HCl as appropriate. A 250 ml culture flask with 100 ml of control algal growth material was filled with an aliquot (1 ml) of the starter to create the stock culture. The volume of stock culture media that needed to be produced was decided by the number of test flasks that would later be inoculated from the stock.
The culture that had been prepared was kept at 25 ± 3 ^°^C with constant “Cool-White” fluorescent lighting (195.08 μM/m²/s). During the algal cell development phase, the cells were gently shaken twice each day. Algal cells that were suited for the experimental technique had a generation duration of 4–7 d.
The algal population was subjected to a range of concentrations of the two compounds for 96 h in a static system using the USEPA approach (EPA 2002). The population’s response was measured in terms of fluctuations in cell density, or counts per millilitre. To prepare test solutions at the desired concentrations, a stock solution of the herbicides under evaluation, the test medium, and 1 ml of inoculum culture were combined. The experiment started when the alga was placed in the test flask. The test solution (100 ml) in 250 ml flasks was distributed at random to reduce the potential impact of spatial variations in temperature and light on the rate of development. After 2 h following the inoculation, a final examination of the cell density in three test solutions was conducted at zero time. The test flasks were manually shaken twice a day for 96 h while being continuously illuminated at 195.08 μM/m²/s and 25 ± 3 ^°^C. The concentration range was determined using the results of range-finding tests. These concentrations ought to fall between 0 and 90% of the range that inhibits the growth of algae. There were 5 replicates used as the control and 3 replicates made for each test concentration. After the experiment was completed, the algal growth in each treatment and the control was counted using a haemocytometer under a light microscope.
Probit analysis was used to estimate EC_50_ for each examined herbicide (Finney 1971). However, the average growth rate was calculated according to the following equation (OECD 2002).
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mu }_{i-j}=\frac{\mathrm{ln}\,{{\rm{B}}}_{j}-\,\mathrm{ln}\,{{\rm{B}}}_{i}}{{{\rm{t}}}_{j}-{{\rm{t}}}_{i}}\cdot {d}^{-1}$$\end{document}The average growth rate is calculated from the time point i to j, where i represents the start of the period (zero time) and j represents the end of the period (96 h). Bi denotes the initial cell number per ml at the start of the experiment, while Bj indicates the cell number per ml after 96 h.
Percent inhibition of growth rate was calculated according to the following equation:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rm{I}} \% =\frac{{\mu }_{c}-{\mu }_{T}}{{\mu }_{c}}\times 100$$\end{document}Where I% is the percent inhibition in average growth rate, is the mean value for growth rate in the control, and is the value for growth rate in the treatment.
At 3 distinct concentrations, 0.1, 0.025, and EC_50_, the effects of the herbicide and its nano-form on the algal biochemical process were examined. The acute toxicity experiment was conducted in accordance with the previously mentioned guidelines. After 96 h, the algal biomass from the treatments and the control was gathered to obtain the required measurements.
Chemical composition, enzymes assays, and osmolyte estimations
The following protocols were used for conducting total protein content, carbohydrate, and lipid peroxidation (LPO), enzyme assays (catalase, CAT; superoxide dismutase, SOD; and ascorbic peroxidase, APx), and osmolytes (free proline content and sucrose content).
The protein content was calculated using the Lowry et al (1951) technique. A boiling water bath was used to heat 1 ml of algal biomass and 1 ml of 1 N NaOH for 10 min. After combining 5 ml of reagent A (made by putting 1 ml of recently made 1% Na-K tartrate solution containing 0.5% CuSO₄ into 50 ml of 2% Na₂CO₃ solution), each ml of the extract was combined, and the mixture was allowed to sit at room temperature for 10 min. Following a thorough mixing in the shaker, 5 ml of reagent B (Folin reagent) was added. This was again incubated for 30 min at room temperature. At 650 nm, the absorbance was measured in a double-beam UV-VIS spectrophotometer (Heλios α v 4.6 Serial No. 111518 Thermo Spectronic) using the Folin reagent as a blank. Algal biomass was expressed as mg/g fresh weight (FW). Bovine serum albumin (BSA) was used as a standard.
The anthrone reagent method was used to determine the amount of carbohydrates (Stainer et al. 1971). A 1.25 ml aliquot of double-distilled water was combined with 1 mg of dried algal biomass. Each sample was treated with 4 ml of anthrone reagent; the blank and standard were incubated for 8 min in a boiling water bath and then allowed to cool for 10 min at room temperature. At 620 nm, the absorbance was measured in relation to the blank. The mg/g dry weight (DW) was used to describe the amount of carbohydrates. The standard utilised was sucrose.
Malondialdehyde (MDA) was measured using the thiobarbituric acid (TBA) method to determine the LPO (Heath and Packer 1968). After homogenising 25 mg of algal biomass in 1.5 ml of 1% trichloroacetic acid (TCA), the mixture was centrifuged for 5 min at 4 ^°^C and 3000 rpm. Four ml of 0.5% TBA were combined with one ml of the supernatant. After 30 min of heating at 95 ^°^C, the mixture was rapidly chilled in an ice bath. After 10-min centrifugation at 4000 rpm, the absorbance at 532 nm was measured. The extinction coefficient (155 mM/cm) of the algal biomass was used to express the MDA level as mM/g DW.
In 1.5 ml of extraction buffer [(50 mM Tris HCl; pH 7.8), 1 mM EDTA, 1 mM MgCl_2_, and 1% polyvinylpyrrolidone (PVP)], as well as 1 mM ascorbate in the case of the APX test, algal cells were extracted by centrifugation and lysed by ultrasonication (Vwr International 89032-216 Water Bath, USA).The homogenate was centrifuged for 20 min at 12,000 rpm. Nasir et al (2015) employed the supernatant as a source for the assessment of the CAT, SOD, and APX enzyme activities. The H_2_O_2_ (1.0 ml) and buffer (1.9 ml) were added to each cuvette. Temperature equilibrium was reached by incubating the mixture in the spectrophotometer for 4 min. A 100 μL sample was then added to the mixture in the cuvette. From 2 to 3 min, the absorbance was measured at 240 nm. The method of assaying the CAT activity followed, and the activity was expressed as U/mg protein (Beers and Sizer 1952).
The activity of SOD was determined using the method described by Winterbourn et al (1975), which is based on the decrease in absorbance of nitro blue tetrazolium (NBT) at 560 nm. In each cuvette, 2.550 ml of the prepared buffer, 0.2 ml of EDTA, 0.1 ml of NBT, and 0.05 ml of riboflavin were added. Then, it was incubated in the spectrophotometer for 4 min to a low temperature equilibration. After that, a 100 μL aliquot was introduced, and the absorbance was monitored for a period of 2–3 min. SOD activity was calculated and expressed as units per mg of protein (U/mg protein).
The ascorbic peroxidase (APX) assay was employed using the Nakano and Asada (1981) method. The spectrophotometer was adjusted to 290 nm at 25 ^°^C. In each cuvette, 1.9 ml of prepared buffer, 1.0 ml of ascorbate and reagent (H₂O₂) were added. The cuvette was incubated in a spectrophotometer for 4 min to achieve temperature-equilibrated conditions. Then, 100 μL of enzyme source were added to the mixture. The absorbance was conducted for 2–3 min, and the activity of the enzyme was expressed as U/mg protein. The value, 2.8 mM, is the extinction coefficient.
Free proline content was determined according to Bates et al (1973). The dried algal material (50 mg) was homogenised with 10 ml of 3% sulfosalicylic acid. An aliquot (2 ml) was mixed with the same volume of each glacial acetic acid and ninhydrin reagent solution. The mixture was boiled in a water bath at 100^°^C for 30 min. After cooling, 4 ml of toluene were added to the mixture. The organic toluene layer (containing the chromophore) was read at 520 nm using toluene as a blank. The amount of proline was calculated as mg/g DW.
Sucrose was determined by using anthrone reagent (Victor et al. 2011). After homogenising 10 mg of dried material in 10 ml of 80% ethanol, it was heated for 10 min in a water bath. Following cooling, 10 min at 100 ^°^C were spent with 0.1 ml of 30% aqueous KOH added. For the mixture, 3 ml of anthrone reagent was added, incubated at 38^°^C for 20 min, and measured at 620 nm. The amount of sucrose was expressed as mg/g FW. The glucose was used as a standard.
Statistical analysis
All data were presented as Mean ± SE; analysis of variance (ANOVA) was used, and values were considered significant at P < 0.05. COASTAT program was used to analyse data (Cohort Software Inc. 1985), and Microsoft Excel (Microsoft 2000) to manage all data. EC_50_ values were expressed with confidence limit (CL) and slope using probit analysis (Finney 1971).
Results and discussion
Nano-form assessment
The freeze-thaw cycle test revealed that the synthesised nano-emulsion was stable. There was no formation of creaming or floating phases. Additionally, following centrifugation or shaking procedures, no separation phase was seen. TEM images declared that particles ranging from 47 to 71 nm had a spherical form. The FTIR pattern of THIO formed a profile which was roughly identical to its nano-derived version as described by Abdel-Halim et al (2021). According to literature, the typical droplet size of an o/w nano-emulsion was between 10 and 500 nm (Anton and Vandamme 2009; Sharma et al. 2010; Solans and Solé 2012; Gupta et al. 2016). Our results support that droplet sizes from 20 to 200 nm indicated good nano-emulsion (Singh and Vingkar 2008; Ostertag et al. 2012; Massoud et al. 2018). The TEM method was also useful for characterising nano-emulsion as a supplementary means of observing oil particles and obtaining accurate details on the morphology of the system. The microscopic image showed the spherical shape of the nano-emulsion, which resembles the look of oil in the aqueous phase. The findings were in line with studies by Singh and Vingkar (2008), Mohammed and Nasr (2020), and Abdhesh and Gupta (2020) on the surface morphology of nano-emulsion using TEM.
Toxicity profile
Toxicity of THIO and nano-form for 96-h exposure was tested on C. vulgaris, and the results are shown in Table 3 and Fig. 1. The conventional form of THIO showed an EC_50_ of 110.06 µg/L, but the nano- form had an EC_50_ of 56.85 µg/L. According to EU guidelines (EU 2011), the traditional and nano-form of the chemical have risk categories R50/R53 based on the magnitude of EC_50_ values. This pattern suggests that the two variants were very toxic to the organism under examination.Table 3. Acute toxicities of thiobencarb and its nano-forms (µg/L) on the freshwater alga, Chlorella vulgaris after 96-h exposureHerbicideEC_50_ (µg/L)CL^^VL^^S^^Thiobencarb110.0636.55–626.140.181.77Nano-form of Thiobencarb56.8528.45–134.260.091.22^^CL: 95% Confidence limit; ^*****^VL: variance of slope and ^*******^S: slopeFig. 1The growth rate (K) pattern (i), and % of inhibition (ii) for thiobencarb and its nano-form on Chlorella vulgaris after 96-h exposure
This research offers the first profile to evaluate a nano-herbicide’s toxicity to freshwater algae. Actually, little research has been done on the toxicity of nano-herbicides on algae, which are an excellent model for evaluating the ecotoxicology of chemicals and the primary component of the ecosystem’s food chain. Pollutant exposure can cause major morphological and biochemical changes in the algal cells. According to the current study, THIO and its nano-form pose a threat to the alga under study. According to EU rules, this behaviour indicated that the chemical employed was extremely harmful to algae. Herbicides typically cause the most harm to aquatic plankton (EU 2011). For example, Li and Mcintyre (2024) examined 25 chemical structure classes and 11 distinct mechanisms of action in order to determine the relative toxicity of 36 herbicides on green algae. A 72-h algal growth suppression test revealed that herbicides that target acetolactate synthase (ALS), microtubule assembly, photosystem II (PSII inhibitors), very-long-chain fatty acid (VLCFA) synthesis, and lipid synthesis are very hazardous. The range of the 72-h EC_50_ values was 0.003 to 24.6 mg/L.
The nano-form of THIO showed effects on algal cell population at doses ranging from 5.0 to 320.0 µg/L compared to the control group. At concentrations ranging from 5–160 µg/L, the growth rate (K) ranged between 1.247 and 1.061. However, at 320 µg/L, it was increased by 0.31 compared to the control group (1.32). For the same doses, K values in the classic form varied from 1.19 to 0.27 (Fig. 1i). The nano-form’s inhibition percentage ranged from 3.2 to 78.2%, whereas the traditional form’s range was 9.85 to 79.5% for the numbers described above (Fig. 1ii).
Biochemical macromolecules
Chlorella vulgaris’ osmolyte components and antioxidant capacity were dramatically reduced after exposure to THIO and its nano-derived form. Protein levels reduced in comparison to the control (276.92 mg/g FW) (Fig. 2). Furthermore, EC_50_ had a reduced protein content of 81.08 and 172.31 mg/g FW, respectively. The same trend was seen for the 0.1 and 0.025 EC_50_ treatments (124.69, 186.15 mg/g FW and 138.54, 196.15 mg/g FW). Carbohydrate content was higher than in the control group, but it remained below 187.76 mg/g DW (Fig. 3). With EC_50_ treatment, the results for THIO and its nano-derived form were 345.59 and 200.75 mg/g DW, respectively. The pattern was comparable for both treatments (0.1 and 0.025 EC_50_).Fig. 2. Protein content (mg/g FW) in Chlorella vulgaris after treatment with thiobencarb and its nano-form for 96 h. Each value is the average (± SE) of three replicates. At the 0.05 probability level, the identical letters denote no significant differenceFig. 3Carbohydrate content (mg/g DW) in Chlorella vulgaris after treatment with thiobencarb and its nano-form for 96 h
The current study found that, in comparison to their controls, THIO and its nano-derived form significantly impaired the synthesis of total protein and carbs. Significant increases (P < 0.05) were also seen in the elements of osmolyte, MDA, and antioxidant enzymes. THIO and its nano-form reduced the amount of protein in the cells. A number of factors may contribute to the inhibition of aromatic amino acid synthesis, which in turn prevents nucleic acid metabolism and protein synthesis. Changes in gene expression may be the cause of these changes in the protein synthesis of stressed algal development (Vivancos et al. 2011). A decrease in protein can also result from a faster rate of amino acid breakdown or from a lack of protein synthesis. Aminotransferases would then feed the amino acids into the tri-carboxylic acid (T-CA) cycle to meet the increased energy demands of a stressful environment (Singh and Bhati 1994). Other effects may include increased and decreased protease activity in the absorption of nitrogen and carbon under stressful conditions (Kumar et al. 2008). Tomlin (1994) was the first to describe the final result after some algae were exposed to THIO. Additionally, after being exposed to 3 mg/L of THIO, Anabaena variabilis showed a substantial drop in total protein concentration, according to Battah et al (2001). Salman et al (2016) found that exposure to 20 mg/L decreased the protein of Oscillatoria limnetica by 37.45%. On the other hand, after 96 h of treatment, C. vulgaris’s protein and carbohydrate contents significantly increased when exposed to pendimethalin (PAD) and its nano-form at sub-lethal EC_50_ concentrations (Noaman et al. 2020). The protein content of Microcystis viridis cells treated with glyphosate at dosages of 0.2, 2, and 5 mg/L on day 3 did not significantly change, according to another study. However, compared to the control cells, the cells treated with 10 mg/L exhibited a much decreased protein content (Ye et al. 2019). According to the current findings, the protein concentration of the traditional formulation was significantly lower than nano-form (Fig. 3). This outcome could be due to variations in the chemical composition for each or to the additions made. The variables were obtained from their FTIR patterns, which showed that some of the typical THIO by-products had stretching peaks that were larger than their nano-form and ranged from 1400 to 800 cm⁻¹ as described by Abdel-Halim et al (2021).
MDA response levels rose above the control (0.01 mM/g DW) with THIO and its nano-derived form treatment. On the other hand, MDA levels rose during EC_50_ treatment; the average values for THIO and its nano-derived form were 0.65 and 0.07 mM/g DW, respectively. In a similar vein, the mean values of the 0.1 and 0.025 EC_50_ treatments were 0.16, 0.03 mM/g DW, and 0.07, 0.01 mM/g DW, respectively (Fig. 4). The study’s treatments increased the amount of carbohydrates compared to the untreated control group. These findings are in line with PAD, which increased carbohydrate content in Protosiphon botryoides (Shabana et al. 2001). Anabina variabilis similarly had a similar profile following exposure to THIO (Battah et al. 2001). However, Shabana and Abou-Waly (1995) claimed that the decrease in carbohydrate content of Nostok muscorum is related to an obstruction of algal photosynthesis, which coincided with the suppression of chlorophyll production at greater triazine concentrations. The increased quantities of carbohydrates in algae exposed to herbicides may be due to increase in sugar content acting as an adaptive response to toxicant stress. It is commonly recognized that certain environmental factors limit the synthesis of proteins, algal cells change and depending on their genotype, can now synthesis either lipids or carbohydrates (Noaman 2007)Fig. 4. Levels of MDA (mM/g DW) in Chlorella vulgaris after exposure to thiobencarb and its nano-form for 96 h
The results showed that MDA levels increased in treatments relative to the control group. MDA is a good measure of oxidative stress and increases in tandem with herbicide concentrations, as previously indicated. This correlation suggests that higher herbicide exposure may exacerbate oxidative damage in plants. Consequently, further research is warranted to explore the specific mechanisms through which herbicides influence MDA production and overall plant health. The current findings correspond with those of Warr et al (1985), who discovered that bentazon herbicide dosages increased, and also MDA content of N. moscurum, A. variabilis, Aulosira fertilissima, and A. cylindrical. Fodorpataki et al (2009) demonstrated that diuron had no discernible effect on the amount of MDA in alga, Scenedesmus opoliensis. However, effect increased more than twice in the methyl viologen and glufosinate present as in the control. This theory proposed that these herbicides harm membranes and interfere with transmembrane transport processes by changing the structural characteristics of the lipid bilayer. In a different study, Manikar et al (2013) treated A. variabilis cultures with 25, 50, 75, and 100 µg/ml of malathion. They significantly raised MDA levels to 63, 86, 115, and 152%, respectively, in contrast to the untreated control. According to Zhao et al. (2017), the herbicide topramezone significantly increased the MDA level in C. vulgaris. In another investigation, exposure to herbicides: metribuzin and bifenox for 24 h caused a notable increase in MDA levels in the microalga, Chlamydomonas reinhardtii (Almeida et al. 2019). In fact, the pesticides cause oxidative stress by generating ROS, such as O_2_^·^, O^.-^, and H_2_O_2_, which lead to the development of LPO in the algal cells. The algal cells are unable to eliminate the excess ROS, which ultimately results in cell damage. Cell membranes are composed of unsaturated phospholipids, which are prone to oxygen radical destruction and can result in an accumulation of MDA (Hong et al. 2008; Qian et al. 2009).
Antioxidant enzymes
Enzyme CAT activity response to THIO and its nano-form increased, surpassing control (mean; 2.90 U/mg protein) (Fig. 5). Also, EC_50_ treatment enhanced activity: 29.17 and 18.07 U/mg protein, respectively. The enzyme’s activity was enhanced by their treatments (0.1 and 0.025 EC_50_) in a similar manner, with mean values of 21.74 and 11.26 U/mg protein and 16.54 and 5.44 U/mg protein, respectively.Fig. 5. Catalase (CAT) activity (U/mg protein) in Chlorella vulgaris after treatment with thiobencarb and its nano-form for 96 h
EC_50_ enhanced the activity, where the mean SOD activity (control; 0.50 U/mg protein) increased with values of 3.05 and 1.47 U/mg protein for THIO and its nano-form, respectively (Fig. 6). Furthermore, the mean values of 2.10, 1.29 U/mg protein and 1.53, 0.51 U/mg protein, respectively, indicated an increase in enzyme activity at 0.1 and 0.025 EC_50_ levels.Fig. 6. Superoxide dismutase **(**SOD) activity (U/mg protein) in Chlorella vulgaris after treatment with thiobencarb and its nano-form for 96 h
Activity of APX increased (Control; 3.91 U/mg protein) (Fig. 7). For THIO and its nano-form, the EC_50_ treatment increased the activity with mean values of 60.46 and 10.22 U/mg protein. Additionally, for THIO and its nano-form, 0.1 and 0.025 EC_50_ treatments boosted activity with mean values of 28.45, 8.19 U/mg protein and 12.26, 3.84 U/mg protein, respectively.Fig. 7. Ascorbate peroxidase (APx) activity (U/mg protein) in Chlorella vulgaris after treatment with thiobencarb and its nano-form for 96 h
The organisms are shielded from the potentially harmful effects of ROS by a variety of antioxidant chemicals and enzymes (Velisek et al. 2019). As an example, O. limentica increased SOD activity, indicating that glyphosate stress may have increased ROS generation. Similar results were reported by Romero et al (2011), who noted that the addition of glyphosate in the C. kessleri growing medium increased enzymatic defense. Similar findings were reported by Smedbol et al (2018), who revealed that samples treated with 500 and 1000 mg/L of glyphosate had higher APX activity, and that phytoplankton cells exposed to the highest dose of glyphosate had greater SOD and CAT activities than the control.The M. viridis cells treated with glyphosate at 0.2 and 1 mg/L showed an increase in SOD activity (Ye et al. 2019). Also, the glyphosate formulation’s action on S. vaculatus microalga was strengthened by its presence of a surfactant (alkyl aryl polyglycol ether). The increased amount of the enzyme then suppressed ROS formation, helping algal cells to tolerate herbicide stress (Bagchi et al. 1995; Peixoto 2005). The results of Prasad et al (2005) and Kumar et al (2008), who found that endosulfan exposure increased SOD, CAT, and APX activities in P. boryanum, A. variabilis, A. fertilissima, and N. moscurum, are supported by the present research. likewise, when cyanobacteria were exposed to higher amounts of malathion, the activity of the three antioxidant enzymes increased noticeably (Manikar et al. 2013). Anabina Cylindrical stress responses to sub-lethal concentrations of bentazon (0.75–2 mM) resulted in a rise in SOD activity after 72-h exposure (Salman et al. 2016). In cultures of C. vulgaris, glufosinate and paraquat enhanced SOD activity at 0.5 mM by 3–4 times (Qian et al. 2008 and 2009). Elevated antioxidant enzyme activity implies that cell components might be shielded from oxidation, as was previously mentioned. Furthermore, it was suggested that the presence of peroxides, including H₂O₂, increased the consistently elevated MDA level, suggesting that the antioxidant enzymes triggered by glufosinate would not be able to eliminate ROS quickly (Qian et al. 2008). According to Almeida et al (2019), herbicides such as biofenox and metribuzin, even at low doses, significantly elevated ROS levels in freshwater alga, C. reinhardtii. Significant differences were seen between the antioxidant enzymes of treatments and the control group (Iummato et al. 2019). Significant increases in oxidative stress markers: ROS, MDA, carbonyl protein (CP), glutathione content (GSH), and carotenoids were seen in the algal cells treated with 6–8 mg/L of glyphosate. However, a significant decrease in CAT and SOD was found in the studied alga. Notably, C. kessleri algal cells treated to doses of 50–200 µg/L of the herbicide S-metolachlor showed increases over the course of 4 d (Maronić et al. 2018). When Pseudokirchneriella subcapitata algal cells were exposed to 115 and 235 μg/L of this herbicide, antioxidant molecules (carotenoids and GSH) were found to be lower in the cells, and SOD and CAT activity was also decreased (Machado and Soares 2021). However, a significant decrease in CAT and SOD was found in the studied alga. Notably, C. kessleri algal cells treated with doses of 50–200 µg/L of the herbicide S-metolachlor showed increases over the course of 4 d (Maronić et al. 2018). When Pseudokirchneriella subcapitata algal cells were exposed to 115 and 235 μg/L of this herbicide, antioxidant molecules (carotenoids and GSH) were found to be lower in the cells, and SOD and CAT activity was also decreased (Machado and Soares 2021). These findings suggest that exposure to S-metolachlor can significantly disrupt the antioxidant defence mechanisms in algal cells, potentially leading to increased susceptibility to oxidative stress (Chin et al. 2019). Further research is necessary to elucidate the underlying biochemical pathways affected by such herbicide exposure.
In fact, oxidative stress has been widely considered as one of the dominant mechanisms in the toxic effects of NMs on algae (Santschi et al. 2017; Chen et al. 2019). Such substances have unique physicochemical properties (e.g., photocatalytic, oxidative capability) which may trigger ROS formation in algal cells via direct and indirect chemical interactions (von Moos and Slaveykova 2014). Studies revealed that the amount of NMs-generated ROS exhibited linear correlations with their toxicity to biological organisms (Li et al. 2012). For instance, exposure to nano-silver had increased the ROS generation in C. vulgaris and strongly resulted in toxic effects (Hazeem et al. 2019). Also, Noaman et al (2020) demonstrated the ability of nano-PAD in C. vulgaris to induce oxidative stress in algal biomass as indicated in alterations of the defence enzyme system and increased generation of MDA.
Osmolyte response
The mean proline content increased as a result of the treatments (Table 4). The mean values of 1.59 and 0.79 mg/g DW were noted for THIO and its nano-derived version, respectively. The EC_50_ treatment demonstrated a rise in proline content. Similarly, the values for THIO and its nano-derived form were obtained with 0.1 and 0.025 EC_50_ treatments, respectively: 1.52, 0.72 mg/g DW and 1.27, 0.72 mg/g DW.Table 4. Osmolytes components in the algal biomass of Chlorella vulgaris after treatment with thiobencarb and its nano-derived form for 96 hHerbicideConcentrationEC_50_0.1 EC_50_0.025 EC_50_MeanProline (mg/g DW^^*)*THIO1.59^a^ ± 0.121.52^a^ ± 0.101.27^b^ ± 0.041.46^a^ ± 0.09Nano-form of THIO0.79^a^ ± 0.070.72^b^ ± 0.080.72^b^ ± 0.090.74^c^ ± 0.08Control**–––0.71^c^ ± 0.09Sucrose (mg/g FW*^^)*THIO2.18^a^ ± 0.011.57^b^ ± 0.151.40^b^ ± 0.191.72^b^ ± 0.12Nano-form of THIO2.84^a^ ± 0.151.97^b^ ± 0.061.71^c^ ± 0.122.17^b^ ± 0.01Control**–––1.36^c^ ± 0.20^A^Each value is the average (± SE) of three replicates^B^At the 0.05 probability level, the identical letters denote no significant difference^C^DW=dry weight. ^**^FW=fresh weight
Treatments resulted higher sucrose content (Control; 1.36 mg/g FW). THIO and its nano-derived version showed mean values of 2.18 and 2.84 mg/g FW, respectively, indicating a rise in sucrose levels following the EC_50_ treatment. Similarly, for THIO and its nano-derived version, the 0.1 and 0.025 EC_50_ treatments showed the following values: 1.57, 1.97 mg/g FW and 1.40, 1.71 mg/g FW, respectively.
Numerous plants, animals, and microbes accumulate osmolytes-tiny organic substances to protect themselves from stress. In this study, the herbicides increased osmolytes such as sucrose and proline. Proline content increased as the concentration of the investigated herbicide increased. These results are in line with those of Fayez and Abd El-Fattah (2007), who discovered that diuron increased the levels of proline and amino acids expressed on a cell dry weight basis in C. vulgaris at varied herbicide dosages. Similar results were obtained by Manikar et al (2013), who showed a significant increase in proline at malathion concentrations of 25, 50, 75, and 100 μg/mL as compared to the untreated control control of A. variabilis. Also, significant increases in proline and sucrose contents were induced in C. vulgaris after exposure to different concentrations of PAD and nano-form for 96 h (Noaman et al. 2020). According to published research, proline is an important antioxidant molecule and one of the essential amino acids that adapts to a variety of environmental stressors. According to Ashraf and Foolad (2007), proline may be a signal or regulatory molecule that initiates a range of physiological and molecular processes. It scavenges ROS and other free radicals, according to a number of reports (Hong et al. 2000; Okuma et al. 2004; Chen and Dickman 2005; Choudhary et al. 2007). Proline′s content increased, indicating its function as ROS scavenger. According to the current findings, proline content rose under both herbicide formulations in a dose-dependent way. These findings unequivocally demonstrated that, when taking the proline parameter into account, the size of the particles in the emulsion was a critical factor in determining toxicity (Lopes et al. 2014). Recently, it was shown that N. muscorum included proline in a mechanism for scavenging free radicals that was activated by the herbicide molinate (Warr et al. 1985). The present results disagree with those of Horie et al (2009), who found that proline levels in C. vulgaris increased after 96 h of ZnONP treatment. Additionally, the same species treated with Fe_2_O_3_NPs had a much higher level of proline content than the bulk equivalent (Saxena et al. 2021).
The current data showed that the sucrose content likewise steadily increased. Soluble sugar accumulation is linked to a variety of stress conditions, including low temperature, salt stress, thirst, and excess excitation energy. All these conditions can either directly or indirectly lead to the buildup of ROS. Higher plants have adaptive responses to stressful situations (Roitsch 1999). Metabolism of sugars and carbon skeletons are prerequisites for synthesis of many compounds involved in antioxidative defence. Furthermore, the main carbon precursor for carotenoid is glucose (Pallett and Young 1993). It is also necessary for the synthesis of ascorbate and carbon skeletons of amino acids, which are the building blocks of GSH and include cysteine, glutamic acid, and glycine (Noctor and Foyer 1998). All these substances are considered defences against oxidative stress due to their roles in redox homeostasis, ascorbate-GSH cycles, peroxide detoxication, and singlet oxygen protection (Cou´ee et al. 2006; Machado and Soares 2020). These findings suggest that these osmolytes might be able to prevent oxidative damage.
Nano-formulations are a new approach to sustain pesticides application with less pollution. The stability of NPs is affected by zeta potential and particle size, commonly referred to droplet size. The manner of absorbing nano-formulations is significantly influenced by its surface characteristics (Kookana et al. 2014). Furthermore, the rigid distribution of the NPs demonstrates their stability in the aqueous solution. There is a strong correlation between the physio-chemical properties of NPs, including their size, dispersion form, and their toxicological impact on living organisms (Cornelis et al. 2014). Based on all information, bio-safety standards need to be considered for non-target species, such algae and others, before deciding which nano-pesticide approaches to employ. In this study, THIO is offered as a model of demonstrating that risk assessment, particularly for nano-pesticides, is insufficient when based on exposure to sub-lethal concentrations and comparative toxicity between the herbicide’s traditional formulation and its nano-derived form (Kookana et al. 2014; Li et al. 2019; Kah et al. 2021). It is essential to do a thorough analysis of the environmental destiny, toxicity, and long-term effects associated with their use. Because of their unique qualities, regulatory frameworks are crucial for adhering to the precautionary principle. Concerns about the use of nano-agrochemicals must be resolved as soon as possible, before they are widely used (Kumari et al. 2023; Kubiak-Hardiman et al. 2023).
Conclusion
The present work investigated the toxicity index of the traditional herbicide THIO and its nano-derived form on C. vulgaris, which is commonly distributed in freshwater media. The obtained results recognized that THIO and its nano-derived form were very toxic to algae. The osmolyte components and antioxidant capacity of the alga were significantly decreased. Moreover, a decrease in protein content was observed, which was consistent with the negative control. In contrast to the control, the treatments raised MDA and glucose levels. Nano-pesticides are increasingly being researched as alternatives to their traditional formulations because of their promise and the lower dosages they require. The favourable contribution these studies provide to the assessment of these items’ ecological safety makes them noteworthy. Results may differ in other situations because they are based on a laboratory-scale exposure scenario. Measurements of THIO and its nano-form were conducted at levels comparable to those observed in the environment. The first profile to assess a nano-herbicide’s toxicity to freshwater algae is provided by this study. The toxicity of nano-herbicides on algae, which are the main link in the ecosystem’s food chain and a great model for assessing the ecotoxicology of pesticides, has actually received little attention. Before making decisions on nano-pesticide practices, all findings about bio-safety protocols for non-target species, such as algae, must be considered.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
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