Antagonistic and Additive Toxicity Assessment of Quinary Mixtures of Metal and 2,4-D Combinations on Algal Phosphatase Function

 Corresponding author: Ugochukwu Romanus Onyeukwu Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Antagonistic and Additive Toxicity Assessment of Quinary Mixtures of Metal and 2,4D Combinations on Algal Phosphatase Function Ugochukwu Romanus Onyeukwu 1, *, Emeka Sabastine Asiwe 2, MaryRose Ogechi Echeta 1, Chizoba Precious Tony-Nze 1 and Japhet Erasmus Aisoni 3 1 Department of Microbiology, Faculty of Science and Computing, University of Agriculture and Environmental Sciences Umuagwo, Imo State, Nigeria. 2 Department of Biochemistry, Faculty of Science and Computing, University of Agriculture and Environmental Sciences Umuagwo, Imo State, Nigeria. 3 Department of Microbiology, Faculty of life science, Bayero University, Kano, Kano State, Nigeria. GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 Publication history: Received on 02 Setember 2025; revised on 08 October 2025; accepted on 11 October 2025 Article DOI: https://doi.org/10.30574/gscbps.2025.33.1.0388 Abstract The present study comprehensively examined the impact of combined metal and pesticide mixtures on the activity of the hydrolytic phosphatase enzyme in the freshwater alga Chlorella vulgaris. Toxicological assessments entailed evaluating inhibition of phosphatase-mediated conversion of p-nitrophenylphosphate to p-nitrophenol, monitored spectrophotometrically at 410 nm. Both unary and quinary mixtures of Copper (Cu²⁺), Zinc (Zn²⁺), Lead (Pb²⁺), Chromium (Cr²⁺), Cadmium (Cd²⁺), and 2,4-dichlorophenoxyacetic acid (2,4-D) were investigated at predetermined fixed molar ratios. Experimental and model-predicted dose-response analyses revealed that all tested heavy metals elicited concentration-dependent inhibitory effects on phosphatase activity. Notably, Cr²⁺ and Cu²⁺ demonstrated the highest toxicities with IC50 values of 0.19 ± 0.01 mM and 0.28 ± 0.00 mM, respectively, whereas 2,4-D manifested comparatively low inhibition, inducing approximately 20% enzymatic suppression. Quinary mixtures formulated at fixed ratios of 20:20:20:20:20 and 16:21:21:21:21 exhibited differential toxicological profiles wherein the 2,4D/Pb²⁺/Zn²⁺/Cr²⁺/Cd²⁺ mixture consistently presented the highest toxicity. Across the different mixtures, a general decline in toxicity was observed, yet toxicity saturation was reached, indicating uniform toxic effects at higher concentrations. Toxicity index evaluations predominantly signalled antagonistic interactions (TI >>1), aside from one mixture in the 20:20:20:20:20 ratio displaying additive interactions. The modulation of toxicity by 2,4-D within these cocktails notably attenuated metal toxicity yet concurrently amplified its own toxic effects. This dualistic modulation likely arises from differential modes of action, temporal exposure variances, and receptor site interactions among the toxicants. Collectively, the findings underscore the ecological risk posed by metal and pesticide co-exposures to freshwater microalgae, mediated through complex antagonistic and additive enzymatic inhibition mechanisms. Keywords: Toxicological assessment; Enzymatic inhibition mechanisms; Dose-response analysis; IC50 values; Metalpesticide interaction; Ecological risk to microalgae 1. Introduction Chemical mixtures are combinations of two or more chemicals that maintain their individual chemical identities without alteration (European Commission, 2012). Despite most toxicity data used for chemical safety evaluations focusing on single compounds, real-world aquatic environments are typically exposed to mixtures rather than individual chemicals (Belfield et al., 2023 in Yang et al., 1998). Therefore, exposure to chemical mixtures is therefore the norm in environmental toxicology (Jongwoon et al., 2022). Furthermore, traditional toxicity assessments may either evaluate GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 147 the mixture as a whole (top-down approach) or analyze the individual components and their combined effects (bottomup approach). Generally, chemical toxicity evaluations use the bottom-up method, where the toxicities of the separate components are modelled to predict the overall mixture effect (Hernandez, Gil, and Lacasana, 2017). A key challenge in assessing mixtures arises when individual chemicals are present at concentrations below their no-observed-effect level (NOEL), but in combination exhibit unexpected toxic effects (Jongwoon et al., 2022; European Commission, 2012b). Predictive toxicity methods often rely on assumptions like concentration addition (CA) for chemicals with similar modes of action or independent action (IA) for those with different modes. In many aquatic ecosystems, organisms such as microalgae occupy an important position in energy trophic levels and are exposed to organic and inorganic chemical mixtures rather than single pollutants (Aronzon, Peluso, and Coll, 2020; Kovalakova et al., 2020; Topaz et al., 2020; Zhang et al., 2021). The inhibition of phosphatase activity in these microalgae serves as a useful indicator for evaluating the effects of environmental toxicants (Nguyen, Moon, and Lee, 2020; Nunes et al., 2018; Göbölös et al., 2024; Onyeukwu and Edna, 2022a; 2022b; Wenq et al., 2021). The effective concentrations of chemical mixtures can be quantified using the combination index (CI) method, and data analysis is often facilitated by sigma statistical plots. The responses for both individual compounds and mixtures are typically evaluated by metrics such as the median inhibitory concentration (MIC50) or effective concentration (EC50). Aside combination index method, quantitative structure property relationship (QSPR) model can be used to develop mathematical relationships that predict chemical’s physical or chemical property based on its molecular descriptors. For example, QSPR models can used to predict thermodynamic properties such as thermal energy, entropy, and heat capacity of camptothecin drug derivatives, using molecular descriptors which is optimized by genetic algorithms and multiple linear regression methods. The model's predictive ability is said to be validated by cross-validation techniques to ensure accuracy in property prediction (Yue and Li, 2022; Ahmadinejad, et al., 2018). Additionally, is the use of artificial neural networks combined with QSPR modelling to predict properties of chemical mixtures more accurately than experimental methods alone. These advanced QSPR models can help in predicting properties related to new biomass fuels, pollution analyses, or toxicological risks (Mu and Li, s2022). Quantitative structure-property relationship (QSPR) models serve as predictive tools to estimate the physicochemical properties of pure compounds and mixtures. To address gaps in QSPR predictions, new formulations and models have been developed to address some inconsistences in QSPR usage. Molecular descriptors used in these models can be constitutional, topological, geometric, or quantum chemical in nature. QSPR and QSAR models are increasingly applied in toxicology, ecotoxicology, pharmaceuticals, pharmacodynamics, pharmacokinetics, chemometrics, and physicalchemical property predictions (Hassold et al., 2021; European Commission, 2020, 2012b; Hernandez et al., 2017; WHO, 2017). A major difference between QSAR modelling of single chemicals and mixtures involves the types of molecular descriptors used, which are critical for accurately predicting toxicity, especially when dealing with nonlinear properties. Common mathematical techniques in QSAR/QSPR include Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), and Support Vector Machines (SVM), which continue to be refined with advanced algorithms and hybrid methods. In this study, the focus is to evaluate both individual and combined effects of single and quinary mixtures of metal ions Copper (Cu2+), Zinc (Zn2+), Lead (Pb2+), Chromium (Cr2+), Cadmium (Cd2+) alongside 2,4dichlorophenoxyacetic acid on the alkaline phosphatase activity of Chlorella vulgaris, using a toxic index model. The interaction effects of mixtures based on concentration were assessed using the toxic index (TI), which sums the toxic units of each component (Boillot and Perrodin, 2008). The TU values were calculated using the expression: I i iIC Cmix TU 50 =  = = n 1i i TUTI , While TI is the summation of TU for n toxicants in the mixture, where Cmixi is the concentration of the ith toxicant in the mixture and EC50i is the EC50 of the same toxicant when tested singly. TI=1 implies additive interaction, TI > 1 implies antagonistic interaction and TI < 1 implies a synergistic interaction (Boillot and Perrodin, 2008). The synergistic, additive, and antagonistic effects between the heavy chemicals and 2,4dichlorophenoxyacetic acid in various mixtures at different effective concentrations were determined using the combination index (CI) method, aside other methods (Kahatagahawatte and Hara-Yamamura, 2020). 2. Material and methods 2.1. Sample area Ihiagwa is a town situated within the Owerri West Local Government Area in Imo State, located in southeastern Nigeria. It lies approximately 12 kilometers south of Owerri, the state capital. The Otamiri River flows through Ihiagwa, with geographic coordinates at latitude 4°54'14.00" N and longitude 7°08'30.00" E. The river's watershed extends over roughly 10,000 square kilometers (3,900 square miles) and receives an annual rainfall ranging between 2,250 and 2,500 millimeters. The area is predominantly covered by depleted rainforest vegetation, with average temperatures hovering around 27°C (81°F) year-round. Unfortunately, the Otamiri River is subject to pollution from organic waste and chemicals, due to inefficient waste management system in Owerri which exacerbates the river's contamination. This pollution primarily results from intensive human activities, including household waste disposal, agricultural runoff, GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 148 failing drainage infrastructure, factory discharges, and various industrial operations. This has led to increased concentrations of pollutants such as phosphates, nitrates, metals, pesticides, and herbicides in the Otamiri River. Despite these challenges, the river serves as an important alternative source of drinking water when the public water supply is unavailable. To better understand the extent of pollution, the current study focuses on a large segment of the Otamiri River along the Nekede to Ihiagwa stretch. The geographical map of the study area is presented in Figure 1. Figure 1 Digital Geographical map of Otamiri River along the Nekede -. Ihiagwa stretch 2.2. Sampling 2.2.1. Sampling Material Sterile glass bottles, with capacity of 200 ml was used for water sampling collection. They were fitted with ground glass or screw caps. The stopper or cap and neck of the bottle were protected from contamination by suitable cover of sterilized thin aluminum foil. Silicon rubber lines, that could withstand repeated sterilization at 1600C, was used inside the screw caps. After being sterilized, the bottle was aseptically stored without opening the sterilized bottle. The samples were collected at a time interval of ten (10) minutes for each sampling points. The timing was regulated by the use of a stop watch by one of the field assistants. The Imo State Water co-operation shows water for treatment in the upstream of this stretch and many activities such as fishing and sand mining that go on downstream. 2.3. Collecting Sample 2.3.1. Sample collection and transportation: Samples of water were collected in sterile bottles. Care was ensured to prevent accidental contamination of water during collection. The River water was collected from Otamiri River in Ihiagwa, Imo State south-eastern Nigeria. Water samples were collected midstream along the course of the river at three spots (upper, middle and lower-course) with different coordinates (a): (5◦24.25 ״0.32 ׳ N, 7◦0.36 ״ 0.036 ׳E; (b): 5◦24.28 ״ 0.55 ׳N, 7◦0.38 ״0.36 ׳E and (c): 5◦23.55 ״0.20 ׳N, 6◦59.46 ״ 0.39 ׳ E) from a depth of 30 cm and pooled in 1-litre sterile plastic bottle or 200 ml sterile glass bottle. Immediately after collection, samples were placed in an insulated cold box or cooler for transport to a water testing laboratory. Water samples were examined upon arrival, 6 hours of collection to ensure continual viability of cells. 2.4. Isolation The pooled sample were stored in a cooler and taken to the laboratory. The algal load of the sample was determined by washing or centrifugation method and agar-plated within six hours of collection. Within the period the water sample was collected, pure cultures of the test organism were isolated and 2-3weeks thereafter for axenic cultures, that was stored under standard microbiological conditions before toxicity assay. The algal load of the water sample was estimated at colony forming unit (CFU/ml). 2.5. Algological analysis 2.5.1. Preparation of agar plate Agar plates were prepared by dissolving 2% agar (w/v) in BBM. The plates were autoclaved at 1260C for 15 minutes. The plates were allowed to cool down and 5 ml warm agar medium was put in the plates. The plates were allowed to cool, kept in inverted position for complete removal of steam and drying at least 72 hrs before streaking in bold basal medium (modified with highly enriched trace metal solution and F/2 vitamin solution), (Stein, 1973). Modified bold basal media plates contained (g/ml); KH2PO: 48.75g/500(10ml), CaCl2•2H2O: 12.5g/500ml (1ml), MgSO4•7H2O: 37.5g/500ml (1ml), NaNO3: 125g/500ml (1ml), K2HPO: 4.37.5g/500ml (1ml), NaCl: 12.5g/500ml (1ml), GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 149 Na2EDTA•2H2O: 10g/L (1ml), KOH: 6.2g/L, FeSO4•7H2O: 4.98g/L (1ml), H2SO4 (concentrated): 1ml/L, Trace Metal Solution: 1ml, H3BO3 5.75g/500ml: (0.7), F/2 Vitamin Solution: (optional: cyanocobalamin, biotin, thiamine) (Nichols and Bold, 1965). 2.5.2. Culture of isolate An aliquot of 12ml of washed and centrifuged algal sample was taken from transported pooled samples into the sterile tubes. The tubes were centrifuge at 3000rpm for 15 minutes. The supernatant was removed. The cells were suspended in a fresh sterile water in each tube using vortex mixer (rotated at 1000-1500rpm up to homogeneous suspension). Centrifugation and washing of algal cells were repeated for six times to expel contaminants and most microorganisms present in the algal sample (Parvin and Habib, 2007). A loopful of the labeled isolate was streaked onto bold basal medium agar, supplemented with antibacterial and anti-fungal drugs. Culture plates were kept under fluorescent light. Subsequently, the Culture plates were incubated for 2 weeks at room temperature (28±20C). Thereafter, the culture was transferred into a broth inoculated with bold basal medium contained in 250 ml conical flask. Continuous culture was maintained to sustain algal growth at exponential phase. 2.5.3. Subculture of isolate Axenic culture of test organism was sub-cultured in an inorganic liquid medium prepared as recommended by OECD (1981). 2.5.4. Storage of pure stock culture For optimal yield of test organism(s), axenic broth culture was incubated at temperature (20±20C), under white fluorescent light (3000-4000) lux, on a rotary shaker. New stock culture was initiated at 40C in the dark, in every 40-60 days, by inoculating approximately 5×104 cells ml-1, (Jonsson and Aoyama, 2007). 2.5.5. Preparation and standardization of inoculum A loopful of the isolates, stored in bold basal medium slant in the refrigerator was inoculated into 200ml of bold basal medium broth contained in 500 ml conical flask and incubated on a rotary shaker at 150rpm at room temperature (28±20C), for 24hrours. After incubation, the cells were harvested by centrifugation at 3000rpm for 10minutes, the supernatants were discarded and the sediment which contained the green pigment cells was harvested. The harvested cells were washed twice in sterile distilled water. The cell extracts were standardized in a spectrophotometer to a density of 1.8 at 600nm. 2.6. Morphological Identification of Isolated organism The morphological traits evaluated, comprised of colony morphology, green pigment and chlorophyll a/b production. Morphological analyses were based on type, elasticity and appearance, while colony morphology parameter was based on colour, form, transparency and diameter. The other tests carried out for identification of the isolate included; chlorophyll production, catalase, phosphatase, starch and lipase (Stein, 1973). 2.7. Molecular (Genome) Identification of the Isolated organism The following molecular identification were carried out: DNA extraction, DNA quantification, 18S sequencing Amplification, Assembly and Annotation and Phylogenetic analysis. 2.7.1. DNA extraction Extraction was done using a ZR fungal/algal/bacterial DNA mini prep extraction kit supplied by Inqaba South Africa. A heavy growth of pure culture of the suspected isolates was suspended in 200 microlitre of isotonic buffer into a ZR Bashing Bead Lysis tubes, 750 microlitre of lysis solution was added to the tube. The tubes were secured in a bead beater fitted with a 2ml tube holder assembly and processed at maximum speed for 5 minutes. The ZR bashing bead lysis tube was centrifuged at 10,000xg for 1 minute. Four hundred (4s00) microlitre of supernatant was transferred to a Zymo Spin IV spin Filter (orange top) in a collection tube and centrifuged at 7000xg for 1 minute. One thousand two hundred (1200) microlitre of algal/fungal/bacterial DNA binding buffer was added to the filtrate in the collection tubes bringing the final volume to 1600 microlitre, 800 microlitre was then transferred to a Zymo-Spin IIC column in a collection tube and centrifuged at 10,000xg for 1 minute, the flow through was be discarded from the collection tube. The remaining volume was transferred to the same Zymo-spin and spun. Two hundred (200) microlitre of the DNA Pre-Wash buffer was added to the Zymo-spin IIC in a new collection tube and spun at 10,000xg for 1 minute followed by the addition of 500 microlitre of algal/fungal/bacterial DNA Wash Buffer and centrifuged at 10,000xg for 1 minute. The Zymo-spin IIC GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 150 column was transferred to a clean 1.5 microlitre centrifuge tube, 100 microlitre of DNA elution buffer was added to the column matrix and centrifuged at 10,000xg microlitre for 30 seconds to elute the DNA. The ultra-pure DNA was then stored at -20 degree for other downstream reaction. 2.7.2. DNA quantification The extracted genomic DNA was quantified using the Nano drop 1000 spectrophotometer. The software of the equipment was launched by double clicking on the Nanodrop icon. The equipment was initialized with 2ul of sterile distilled water and blanked using normal saline. Two microlitre of the extracted DNA was loaded onto the lower pedestal; the upper pedestal was brought down to contact the extracted DNA on the lower pedestal. The DNA concentration was measured by clicking on the “measure” button. 2.7.3. 18S sequence Amplification The 18S regions of the isolates was amplified using the 18S C-2 b: 5‘- ATTGGAGGGCAAGTCTGGT-3‘' and 18S D-2 b: 5'- ACTAAGAACGGCCATGCAC-3, Primers on a ABI 9700 Applied Bio systems thermal cycler at a final volume of 30 micro litres for 35 cycles. The PCR mix included: The X2 Dream taq Master mix supplied by Inqaba, South Africa (taq polymerase, DNTPs, MgCl), the primers at a concentration of 0.4M and the extracted DNA as template. The PCR conditions was maintained as follows: Initial denaturation, 950C for 5 minutes; denaturation, 950C for 30 seconds; annealing, 530C for 30 seconds; extension, 720C for 30 seconds for 35 cycles and final extension, 720C for 5 minutes. The product was resolved on a 1% agarose gel at 120V for 15 minutes and visualized on a blue light trans illuminator. 2.7.4. Sequencing Sequencing was done using the BigDye Terminator kit on a 3510 ABI sequencer by Inqaba Biotechnological, Pretoria South Africa. The sequencing was done at a final volume of 10ul; the components includes: 0.25ul BigDye® terminator v1.1/v3.1, 2.25ul of 5 x BigDye sequencing buffer, 10uM Primer PCR primer, and 2-10ng PCR template per 100bp. The sequencing condition was maintained as follows 32 cycles of 960C for 10s, 550C for 5s and 600C for 4min. 2.7.5. Phylogenetic Analysis Obtained sequences was edited using the bioinformatics algorithm Trace edit, similar sequences were downloaded from the National Center for Biotechnology Information (NCBI) data base using BLASTN. These sequences were aligned using MAFFT. The evolutionary history was inferred using the Neighbour-Joining method in MEGA 6.0 (Saitou and Nei, 1987). The bootstrap consensus tree inferred from 500 replicates (Felsenstein, 1985) was taken to represent the evolutionary history of the taxa analysed. The evolutionary distances were computed using the Jukes-Cantor method (Jukes and Cantor, 1969; Saitou and Nei, 1987; and Felsenstein,1985). 2.7.6. Phosphatase Extraction Phosphatase extraction followed the method described by Jonsson and Aoyama (2007). Algal pellets identified molecularly were frozen in liquid nitrogen and macerated in a mortar with acetate buffer. The mixture was subjected to freeze-thaw cycles by storing at −20°C and thawing at room temperature to produce a 1:4 (w/v) suspension. Cell disruption was achieved by probe sonication on ice (0°C) with 50 seconds sonication followed by 20 seconds intervals (1 cycle) at 70% amplitude; repeated twice. The lysate was centrifuged at 10,000 rpm for 20 min. Afterwards, the extract from supernatant was assayed for the conversion of colourless p-nitrophenylphosphate to yellow, p-nitrophenol. The supernatant containing the enzyme extract was identified as alkaline phosphate using litmus indicator and stored to be used for phosphatase assays. 2.8. Toxicity of Unary-Quinary mixtures of metals and pesticide to Chlorella vulgaris alkaline phosphatase activity The individual or single dose of graded ionic concentrations of copper, zinc, chromium, lead, cadmium and 2,4dichlorophenoxyacetic acid was assessed. The metals were each prepared in 10mM stock concentration. The concentration ranges from Copper (Cu2+), Zinc (Zn2+) and Chromium (Cr2+) was (0-0.5mM), Lead (Pb2+) and Cadmium (Cd2+) ions (0-7.0mM) and 2,4-dichlorophenoxyacetic acid (2,4-D) (0-25mM). Quinary mixtures of pesticide/metal were amalgamated in a simple percentage ratio of 20:20:20: 20:20 and 16:21:21: 21:21 for the quinary combinations’ ratios. Their toxicities were determined at concentrations from 0-9.0mM. Inhibitory study on the alkaline phosphate (ALP) activity was determined in 3ml reaction final volume consisting of the graded concentration of single or quinary toxicants mixture, distilled water, buffered enzyme, and substrate (p-NPP), contained in 15ml sterilized culture tubes. The set-up was conducted in triplicates. The control consists of set-ups devoid of toxicants. The set-up contained graded concentrations of toxicants amended in requisite volume of distilled water, 0.5ml buffered substrate p-NPP (pH 8.0) GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 151 and 0.5ml crude enzyme was added and the culture tubes were incubated for 30-40 minutes at room temperature (370C). A 0.1ml of sodium hydroxide was added to stop the reaction. Thereafter, the tubes were centrifuged and 2ml of the supernatants was spectrophotometrically measured at 410nm. 2.9. Statistical Analysis 2.9.1. Estimation of relative response, Median Inhibitory Concentration of UnaryQuinary Combinations of Metals and Pesticide and Modeling of Data from the Inhibition analysis. The relative responses were evaluated as percentage relative test to control, due to inhibition of the toxicants and fitted model equations (Figure 2), Figure 2 Relative responses In equation: (1), RC is the response of the control and RT is the response in the tests (at different concentrations of the toxicant). The data generated from relative inhibition responses of unary-quinary of metals and pesticide were fitted into dose-response models (Cedergreen, et.al., 2005) using sigma plot (version 10). The individual median inhibitory concentration (EC50) of the individual toxicants and toxicants mixture to phosphatase enzymatic activity in Chlorella vulgaris was generated from the models. The fitted models are also elaborated with their equations below (Figure 3). Figure 3 Ppercentage relative control inhibition and fitted model equations Where: • y is the relative response. • y0 is the response at infinite x. • b parameter determining the slope of the hormetic increase, • a is the maximum response. • f is the parameter describing the degree of hormetic increase. • x is the concentration of phenol. • x0 is EC50. 2.10. Analysis of toxicity using toxic index model The toxicity interaction of the mixtures was assessed using the toxic index (TI), which sum the components toxic unit (Boillot and Perrodin, 2008). The TU values were calculated using the expression as shown in figure four: GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 152 𝑇𝑈𝑖 = 𝐶𝑚𝑖𝑥𝑖 𝐼𝐶50𝐼 ....................................................................(6) While TI is the summation of TU for n toxicants in the mixture Figure 4 Toxicity exponent model equations Where Cmixi is the concentration of the ith toxicant in the mixture and IC50I or EC50i is the EC50 of the same toxicant when tested singly. TI=1 implies additive interaction, TI > 1 implies antagonistic interaction and TI < 1 implies a synergistic interaction (Boillot and Perrodin, 2008). The experimental data was fitted and the median inhibitory concentrations (EC50) of individual and mixtures of pesticides and metals were evaluated using Sigma plot software (10.0). Statistical analysis of EC50 values was computed using one-way analysis of variance (p<0.05) in statistical package for the social sciences (IBM, SPSS software 22). 3. Results 3.1. Morphological and biochemical identification of the isolate R1(AY591506). The morphological traits of isolate R1(AY591506) under the electron microscope comprises of a spherical microscopic cell with 2-10μm diameter. In morphometric observations, cell diameter was 3-12 μm. Chloroplast was parietal and cup-shaped with a single pyrenoid the isolate showed many structural elements similar to plants. It has a unilaminar cell wall with subspherical or ellipsoidal shape without flagella. The isolate R1(AY591506) also comprises of a single chloroplast with a double enveloping membrane composed of phospholipids. The singly, stranded-shaped chloroplast with pyrenoids, contains a cluster of fused thylakoids of green chlorophyll pigment. It contains a gel-like substance confined within the barrier of the cell membrane, a double-layer membrane that resembles the mitochondrion and a dense circular patch (fig 5). Under the light microscopy it appeared green, unicellular, and spherical (coccoid) or subspherical. Chloroplast was parietal and cup-shaped with a single pyrenoid. All morphological and phylogenetic tracing with descriptions of R1(AY591506 (fig 6), was tentatively identified as Chlorella sp following the works of Tomaselli, (2004); Borowitzka, (2018); Safi, et al., (2014); Krienitz, et al., (2015); Yamamoto, et al., (2005); Champenois, et al., (2015); Garcia, (2012); Beijerinck, et al., (1890); Yamamoto, et al., (2004). All morphological and biochemical screening are presented in table 1 GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 153 Table 1 Morphological and biochemical screening assay of Chlorella vulgaris Parameters Results Shape Spherical/cup-shaped pyrenoid Form Ellipsoidal Chloroplast (Singleranded) present Colour Green thylakoids pigment Type Unilaminar Cell diameter 2-12 um Phosphatase test ++ Nitrogenase test ++ Chlorophyll analysis ++ Nitriles test ++ Lipoxygenases ++ Amylase + Lipase test ++ Urease test ++ Thiolase/gelatinase test ++ Catalase ++ Hydrogenases ++ Carbonic anhydrase ++ Species: Chlorella vulgaris Figure 5 Schematic Microscopic structure of Chlorella vulgaris 3.2. Molecular (Genome) Identification of the Isolate R1(AY591506). From the molecular identification to specie level using the process of DNA extraction, DNA quantification,18S sequencing Amplification, Assembly, Annotation and Phylogenetic analysis, the obtained 18S sequence from the isolates (R1(AY591506) produced an exact match during the megablast search for highly similar sequences from the NCBI non- GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 154 redundant nucleotide (nr/nt) database. The 18S of the isolates showed a percentage similarity to other species at 99100%. The evolutionary distances computed using the Jukes-Cantor method were in agreement with the phylogenetic placement of 18S of the isolates within the Chlorella sp and revealed a closely relatedness to Chlorella vulgaris respectively Figure 6 Phylogenetic tree showing the isolate Chlorella vulgaris 3.3. Toxicity of Individual Metals and 2,4-D on Chlorella vulgaris Phosphatase Activity Result presented in figure 4 shows the experimental (data points) and model-predicted dose-response data cure for unary toxicity of heavy metals: Copper, Lead, Chromium, Cadmium, Zinc ions and 2, 4-D to Chlorella vulgaris phosphatase activity. The results indicate that the inhibition of phosphatase activity closely fitted into a Sigmoidal 3 Parameter model; with R2 values ranging from 0.98-0.99. Threshold inhibitory concentrations (EC50) of the single compounds are presented in table 2.0. Table 2 Median inhibitory concentration (IC50) of the effect of Unary toxicity of(metal/pesticide) to phosphatase enzyme activity from Chlorella vulgaris. Toxicants IC50 (mM) Toxicity index Interaction Unary (100%) Cu2+ Zn2+ Pb2+ Cr2+ Cd2+ 2,4-D 0.28 ±0.01c 0.31±0.02c 1.86±0.76c 0.19±0.01c 2.59±0.32c 16.82±1.47c - - - - - - Toxic Toxic Toxic Toxic Toxic Toxic GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 161 [28] Göbölös, B., Seb˝ok, R. E., Szabó, G., Tóth, G., Szoboszlay, S., Kriszt, B., Kaszab, E., and Háhn, J. (2024). The Cocktail Effects on the Acute Cytotoxicity of Pesticides and Pharmaceuticals Frequently Detected in the Environment. Toxics. 12, 189. [29] Greco, W. R., Bravo, G and Parsons, J. C. (1995). The search for synergy: A critical review from a response surface perspective. Pharmacological Reviews., 47, (2): 331–385. [30] Greco, W., Unkelbach, H. D., Pöch, G., Sühnel, J., Kundi, M and Bödeker, W. (1992). Consensus on concepts and terminology for combined action assessment: The Saariselkä agreement. Archives of Complex Environmental Studies, 4, (3): 65–69. [31] Hegewald, E.H. (2000). New combinations in the genus Desmodesmus (Chlorophyceae,Scenedesmaceae). Algological Studies. 96,1-18. [32] Hassold, E., Galert,W and Schulze, J.(2021). Options for an environmental risk assessment of intentional and unintentional chemical mixtures under REACH: the status and ways forward, Environ. Sci. Eur. 33 (131). [33] Hernandez, A.F., Gil, F.and Lacasana, M. (2017). Toxicological interactions of pesticide mixtures: an update, Arch Toxicol 91 (10) 3211–3223. [34] Illman, A.M., Scragg, A.H and Shales, S.W.(2002). Increase in Chlorella strains caloritic values when grown in low nitrogen medium. Enzyme of Microbial Technology, 27,631-635. [35] Jonker, M. J., Svendsen, C., Bedaux, J. J., Bongers, M and Kammenga, J. E. (2005). Significance testing of synergistic/antagonistic, dose level-dependent, or dose ratio-dependent effects in mixture dose-response analysis. Environmental Toxicology and Chemistry, 24, 2701–2713. [36] Jonsson, C. M. and Aoyama, H. (2007). In vitro effect of agriculture pollutants and their joint action on Pseudokirchneriella subcapitata acid phosphatase. Chemosphere, 69:849-855. [37] Jukes, T. H and Cantor, C.R. (1969). Evolution of protein molecules. In: H.N Munro, (Ed), Mammalian Protein Metabolism. New York, Academic Press, New York. 21-132. [38] Jongwoom, K., Myungwon, S., Jiwon C. and Minju, N. (2022). MRA Toolbox v.1.0: a web-based toolbox for predicting mixture toxicity of chemical substances in chemical products. Scientific Reports. 12:8880. [39] Kahatagahawatte,Y.B.P. and Hara-Yamamura, H. (2020).Review on Mixture Toxicity of Pharmaceuticals in Environmental Waters and Wastewater Effluents. In Resilience, Response, and Risk in Water Systems; Springer Transactions in Civil and Environmental Engineering; Springer: Singapore, 105–126. [40] Kovalakova, P., Cizmas, L., McDonald, T. J., Marsalek, B., Feng, M and Sharma, V. K. (2020) Occurrence and toxicity of antibiotics in the aquatic environment: A review. Chemosphere, 251, 126351. [41] Kolmar, S. S and Gruike, C. (2021). Determining the predictive Limit of QSAR Models. The United States Environmental Protection Agency’s Center for Computational Toxicology and Exposure.15070. [42] Krienitz, L., Huss, V. A. R andBock, C.(2015). Chlorella: 125 years of the green survivalist. Trends in Plant Science, 20, 67-69. [43] Loewe, S.; Muischenk, H. Kombinationswirkungen. 1. Mitteilung: Hilfsmittel Der Fragestellung (1926). NaunynSchmiedeberg’s Archive Experimental Pathway Pharmakol. , 24, 316–326. [44] Lu, W., Wang, Z., Wang, X. & Yuan, Z. (2015). Cultivation of Chlorella sp. using raw diary wastewater for nutrient removal and biodiesel production: characteristics comparison of indoor bench-scale and outdoor pilot-scale cultures. Bioresource Technology, 1, 1-31. [45] Martínez-Ruiz, E. B and Martínez-Jerónimo, F. (2015). Nickel has biochemical, physiological, and structural effects on the green microalga Ankistrodesmus falcatus: An integrative study. Aquatic Toxicology, 169, 27–36. [46] Mu, Y., and Li S. (2022). “Quantitative Structure-Property Relationship Model Based on Artificial Neural Network”. Journal of Engineering Research and Reports 22 (5):14–24. [47] Mohy El-Din, S. M and Abdel-Kareem. M. S. (2020). Effects of Copper and Cadmium on the Protein Profile and DNA Pattern of Marine Microalgae Chlorella salina and Nannochloropsis salina. Environmental Process, 7,189-205. [48] Nguyen, M. K., Moon, J.Y and Lee, Y. C. (2020). Microalgal ecotoxicity of nanoparticles: An updated review. Ecotoxicology and Environmental Safety, 201, 110781. GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 162 [49] Nichols, H and Bold, H. (1965). Growth media-fresh water. In: Hand Book of Physiology. Methods; Stein, J. R., Ed.; Cambridge University Press: Cambridge, UK, pp. 7-24. [50] Nunes, B. W., Hedlund, K. F., Oliveira, M. A and Carissimi, E. (2018). Ecotoxicological analysis of acrylamide using a microalga as an Indicator organism. Water Environment Research, 90, 442–451. [51] O’Brien, A. L and Keough, M. J. (2014). Ecological responses to contamination: A meta-analysis of experimental marine studies. Environmental Pollution, 195, 185–191. [52] Onyeukwu, R. U., Asiwe, E. S., Nweke, C. O., Vivian, K. G.M.(2023). Toxicity of single and ternary mixtures of heavy metals and 2,4-dichlorophenoxyacetic acid to Chlorella vulgaris alkaline phosphatase activity.World Journal of Advanced Research and Reviews, 17(03), 206–223. [53] Onyeukwu, U. R and Edna, I. C. (2022a). Toxicity of binary mixture of heavy metals and 2,4-dichlorophenoxyacetic acid on phosphatase activity of Chlorella vulgaris. IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT),16, 24-36. [54] Onyeukwu, U. R and Edna, I. C. (2022b). Influence of binary mixture of heavy metals and 2,4dichlorophenoxyacetic acid on phosphatase activity of Desmodesmus abundans. International Journal of Innovative Science and Research Technology, 7,76-87. [55] Parvin, M., Zannat, M.N. and Habib, M.A.B. (2007). Two important techniques for isolation of microalgae. Asian Fisheries Science 20:117-124. [56] Perumal, P., B. Balaji-Prasath., P. Santhanam, S. Ananth, A. ShenbagaDevi2 andS. Dinesh-Kumar.(2012).Isolation and culture of microalgae. Workshop on Advances in Aquaculture Technology. [57] Roell, K.R., Reif, D.M., Motsinger-Reif, A.A. (2017). An Introduction to Terminology and Methodology of Chemical Synergy-Perspectives from across Disciplines. Frontiers in Pharmacology, 8, 158. [58] Saitou, N. and Nei, M. (1987). The neighbour-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4:406-425. [59] Schuijt, L.M.; Peng, F.J.; Van den Berg, S.J.P.; Dingemans, M.M.L.; Van den Brink, P.J. (2021). Ecotoxicological tests for assessing impacts of chemical stress to aquatic ecosystems: Facts, challenges, and future. Science of the Total Environment, 795, 148776. [60] Stein,J.R.(1973). Handbook of Phycological Methods.Culture methods and growth measurements. Cambrigde University Press, Cambrigde, 56-60. [61] Safi, C., Zebib, B. Merah, O. and Pontalier, P.(2014). Morphology, composition, production, processing and applications of Chlorella vulgaris: A review. Renewable and Sustainable Energy Reviews, 35,265278. [62] Saxena, P and Harish, (2019). Toxicity assessment of ZnO nanoparticles to freshwater microalgae Coelastrella terrestris. Environmental Science and Pollution Research international, 26,26991-27001. [63] Silva, E., C. Martins, A. S. Pereira, S. Loureiro, and M. J. Cerejeira. (2018). Toxicity prediction and assessment of an environmentally realistic pesticide mixture to Daphnia magna and Raphidocelis subcapitata. Ecotoxicology. 27:956–967. [64] Su, C., Lu, Y., Johnson, A. C., Shi, Y., Zhang, M.,Zhang, Y., Juergens, M. D and Jin, X. (2017). Which metal represents the greatest risk to freshwater ecosystem in Bohai Region of China? Ecosystem Health and Sustainability, 3(2). [65] Tomaselli, L. (2004). The microalgal cell. In: A. Richmond (Ed.), Handbook of Microalgal Culture: Biotechnology and Applied Phycology.Oxford, UK: Blackwell Science press Ltd. [66] Topaz, T., Egozi, R., Suari, Y., Ben-Ari, J., Sade, T., Chefetz, B and Yahel, G. (2020) Environmental risk dynamics of pesticides toxicity in a Mediterranean micro-estuary. Environmental Pollution, 265, 114941. [67] Vanessa A.M., Ariana G. W., Juliano S. G., Gabriel C. W., Cristiane V. T., Héla T., Jean-François F., Claudemir M. R., Cleder A. S and Sylvie C. (2023). An alternative approach to assess ecotoxicological effects of agrochemical combinations used in Brazilian aquaculture farms. Environmental Science and Pollution Research. 30:70713– 70721. [68] Toropova, A.P., Andrey, A.T and Natalja, F.(2022). Quasi-smiles for predicting toxicity of nano-mixtures to Daphnia Magna. NanoImpact, www.sciencedirect.com. 28,100427. GSC Biological and Pharmaceutical Sciences, 2025, 33(01), 146-163 163 [69] Vilela, P., G. Jacome, S. Y. K. Kim, Nam, and C. Yoo, (2020). Population response modeling and habitat suitability of Cobitis choii fifish species in South Korea for climate change adaptation. Ecotoxicology Environmental Safety, 189:109949. [70] Wang, D., S. Wang, L. Bai, M.S. Nasir, S. Li, and W.Yan, (2020) Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review. Frontiers Microbiology, 11:1651. [71] Wang, W., Wu, J., Zhang, X., Hao, C., Zhao, X., Yu, G and Jiao, G. (2017). Inhibition of influenza A virus infection by fucoidan targeting viral neuraminidase and cellular EGFR pathway. Scientific Reports, 7,1–14. [72] Wenq, J., Guojun, Y., Xu, J., Xueke, L., Donghui, L and Zhiqiang, Z. (2021). Effects of Cd2+ and Pb2+ on enantioselective degradation behavior of α-cypermethrin in soils and their combined effect on activities of soil enzymes. Environmental Science and Pollution Research, 28:47099-47106. [73] Wang, Q., Yalin, D. Sun, F. Deng, X and Chang, H. (2020). Ranking Ecological Risk of Metals to Freshwater Organisms in Lake Taihu, China. Hindawi Journal of Chemistry, 6. [74] Weissmannová, H.D., Pavlovský, J. Fišerová, L. and Kosárová, H. (2018). Toxicity of Diclofenac : Cadmium Binary Mixtures to Algae Desmodesmus subspicatus Using Normalization Method. Bulletin of Environmental Contamination Toxicology,101:205-213. [75] World Health Organization (WHO) (2017) Chemical mixtures in source water and drinking-water. [76] Yamamoto, M., Fujishita, M., Hirata, A and Kawano, S. (2004). Regeneration and maturation of daughter cell walls in the autospore-forming green alga Chlorella vulgaris (Chlorophyta, Trebouxiophyceae). Journal of Plant Research, 117, 257-64. [77] Yang, R.S. Thomas, R.S. Gustafson, D.L.(1998). Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling, Environ Health Perspect 106 (6)1385–1393. [78] Yamamoto, M., Kurihara, I. and Kawano, S. (2005). Late type of daughter cell wall synthesis in one of the Chlorellaceae, Parachlorella kessleri (Chlorophyta, Trebouxiophyceae). Planta, 221,766-75. [79] Zhang, F., B. Meng, S. Gao, R. Hough, P. Hu, Z. Zhang, S. Yu, K. Li, Z. Liu, and S. Cui, (2021). Levels, Inventory, and Risk Assessment of Heavy Metals in Wetland Ecosystem, Northeast China: Implications for Snow Cover Monitoring. Water. 13, 2161. [80] Zeb, B., Zheng, P., Qaisar, M., Qiu, L., Arshid, P., Muhammad, I., Muhammad, B., Zulfiquar, A. B and Shahida, S. (2017). Assessment of combined toxicity of heavy metals from industrial wastewaters on Photobacterium phosphoreum, T3S. Applied Water Science, (7), 2043-2050.