Next Article in Journal
A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals
Previous Article in Journal
Research Progress on Molecularly Imprinted Materials for the Screening and Identification of Organic Pollutants
Previous Article in Special Issue
Easy-to-Use Chemiluminescent-Based Assay for a Rapid and Low-Cost Evaluation of the Antioxidant Activity of Cosmetic Products
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Chemosensors
Volume 12
Issue 5
10.3390/chemosensors12050073
chemosensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Biosensor-Based Assessment of Pesticides and Mineral Fertilizers’ Influence on Ecotoxicological Parameters of Soils under Soya, Sunflower and Wheat

by
Ludmila Khmelevtsova
1,
Maria Klimova
1,
Shorena Karchava
1,
Tatiana Azhogina
1,
Elena Polienko
2,
Alla Litsevich
1,
Elena Chernyshenko
1,
Margarita Khammami
1,
Ivan Sazykin
1 and
Marina Sazykina
1,*
1
Academy of Biology and Biotechnology Named after D.I. Ivanovsky, Southern Federal University, 194/2 Stachki Avenue, Rostov-on-Don 344090, Russia
2
Federal State Budget Scientific Institution “Federal Rostov Agricultural Research Centre”, Rassvet, Rostov-on-Don 346735, Russia
*
Author to whom correspondence should be addressed.
Chemosensors 2024, 12(5), 73; https://doi.org/10.3390/chemosensors12050073
Submission received: 1 March 2024 / Revised: 5 April 2024 / Accepted: 11 April 2024 / Published: 2 May 2024
(This article belongs to the Special Issue Chemiluminescent and Bioluminescent Sensors)
Browse Figures
Versions Notes

Abstract

:
Pesticides and fertilizers used in agriculture can negatively affect the soil, increasing its toxicity. In this work, a battery of whole-cell bacterial lux-biosensors based on the E. coli MG1655 strain with various inducible promoters, as well as the natural luminous Vibrio aquamarinus VKPM B-11245 strain, were used to assess the effects of agrochemical soil treatments. The advantages of using biosensors are sensitivity, specificity, low cost of analysis, and the ability to assess the total effect of toxicants on a living cell and the type of their toxic effect. Using the V. aquamarinus VKPM B-11245 strain, the synergistic effect of combined soil treatment with pesticides and mineral fertilizers was shown, which led to an increase in the overall (integral) toxicity of soils higher than that of the individual application of substances. Several probable implementation mechanisms of agrochemical toxic effects have been discovered. DNA damage caused by both SOS response induction and alkylation, oxidative stress due to increased superoxide levels, and damage to cellular proteins and membranes are among them. Thus, the usage of biosensors makes it possible to assess the cumulative effect of various toxicants on living organisms without using expensive chemical analyses.

1. Introduction

Modern agricultural practices include an extensive use of fertilizers and pesticides. Despite achievements in organic farming and the use of biological crop protection products, the complete elimination of chemicals in the foreseeable future seems an unlikely scenario. In this regard, the task of agricultural soils condition monitoring is of particular relevance.
Excessive or improper use of pesticides can lead to the accumulation of agrochemical residues, which then enter the ecosystem and food chain directly or indirectly. In turn, this has a negative impact on living beings, including humans [1,2]. Chemical methods (chromatography, mass spectrometry, etc.) are mainly used to detect trace amounts of chemicals in environmental samples [3,4,5]. The disadvantages of these methods are their relatively high cost, duration of analysis, and equipment requirements. In addition, chemical analysis alone can only provide accurate information about the levels and composition of contaminants but cannot assess their actual toxicity to biological systems.
In this regard, considerable attention has recently been paid to the development of biosensors that can be a good alternative to chemical analysis [6]. Biosensors with various biological parts (whole cells, enzymes, antibodies, DNA and RNA) have emerged as promising tools for environmental research. In particular, whole-cell biosensors can identify environmental risks associated with pollutants using modeling of microbial interactions with pollutant molecules [7]. These sensors respond to pollutant mixtures and can be used as a net effect sensor, including the response to compounds that cannot yet be identified by chemical analysis.
Whole-cell biosensors are biological reporters that convert chemical signals into a detectable signal using microbial cells as sensors. They typically consist of two parts: biological sensors (microbial cells) that generate signals in the presence of a target chemical (group of substances), and physical devices that convert and detect optical, electrochemical and thermodynamic signals [8,9]. Lux-biosensors containing bacterial luciferase genes under the control of specifically inducible promoters are a type of optical biosensors. Lux biosensors can be divided into two groups—those with inducible and those with constitutive luminescence. Sensors of the first group have low background luminescence, which increases in the presence of the target substance (group of substances, during stress exposure), and the second group (often natural luminous strains) has a high level of luminescence, which decreases in the presence of toxicants in the environment.
Despite a large number of reports on the design of various biosensor types, there are much fewer examples of their actual use for environmental objects’ monitoring. Thus, a whole-cell bacterial sensor based on the Acinetobacter baylyi ADP1 Tox2 strain with constitutive luminescence was used to determine the toxicity of river water in Bangladesh [10], and contaminated seawater near wastewater outlets in China [11]. In addition, a biosensor based on the same strain, A. baylyi ADP1_recA_lux, was used to determine the genotoxicity of groundwater contaminated with a mixture of phenolic compounds [12], soils and groundwater contaminated with petroleum hydrocarbons as a result of an accident in Lanzhou, China [13]. Using the E. coli RecA::luxCDABE bioreporter, the genotoxicity of chromium-contaminated soils and seawater contaminated with crude oil after the Jiaozhou Bay spill was studied [14]. Whole-cell bacterial lux-biosensors based on the E. coli MG1655 strain with various inducible promoters were used to monitor the genotoxicity of the Don River bottom sediments (Russia) [15], wastewaters of Rostov-on-Don and Munich [16], bottom sediments of the Sea of Azov [17], and soils of various types of land use [18]. The review carried out by Bazhenov [19] systematizes the information on the use of whole-cell bacterial lux biosensors based on various bacterial strains, including their use in ecotoxicological environmental studies.
In addition to determining general genotoxicity in natural samples, the use of biosensors for specific pollutants detection is also described. Using the A. baylyi ADPWH_alk strain, which contains the alkM operon, the content of available n-alkanes in soil and groundwater contaminated by an accident in China was determined [13], as well as that in soil from an oil pumping field and chromium slag storage facility in China [20], and in seawater and bottom sediments at the site of an oil reservoir explosion in Dalian, China [21]. Whole-cell biosensors based on E. coli were used to determine the concentration of p-nitrophenol in soils near a chemical plant (Nanjing, China) [22] mercury in historically contaminated soils of the Hunan and Guizhou mining areas (China) [23].
Thus, one of the main application areas of bacterial biosensors in environmental studies is the determination of general toxicity/genotoxicity, as well as the presence of specific pollutants. Most often, the objects of study are anthropogenically polluted territories, natural waters (river, sea, underground), and—extremely rarely—agricultural soils. The potential of biotesting using bacterial biosensors can be much wider due to an increase in the range of tested objects and the possibility of using promoters that respond to various groups of pollutants or toxic effects. As a result, we can obtain more complete information about the totality of biological processes occurring in contaminated environmental objects.
In this work, we conducted a study of agricultural soils treated with agrochemicals using a battery of whole-cell bacterial lux-biosensors based on the E. coli MG1655 strain with inducible promoters and the natural Vibrio aquamarinus VKPM B-11245 strain. The natural strain was used to determine the integral toxicity of soils, and the genetically engineered strains of E. coli MG1655 were used to study genotoxic, pro-oxidant, protein- and membrane-damaging properties of soils. Using the set of strains made it possible to determine not only the fact of toxicity of soils with various treatments, but also to suggest a number of biological mechanisms through which the toxic effects of agrochemicals are manifested.

2. Materials and Methods

2.1. Field Experiment

The field experiment was carried out in 2022–2023 in the Rassvet village in the Rostov region, Russia (47°21′40″ N and 39°52′50″ E). The region has a temperate continental climate with an average annual precipitation of 530–550 mm, average monthly temperature from 5 °C to −9 °C in winter, +22–24 °C in summer. Soya (Glycine max), sunflower (Helianthus annuus L.) (2022) and winter wheat (Triticum aestivum) (2023) were grown in four treatments: control (without fertilizers and pesticides), only fertilizers, only pesticides, and combined use of fertilizers and pesticides. The list of soil samples collected during the field experiment is given in Appendix A Table A1.
Ammophos (12:52) was used as a fertilizer on soya and sunflowers for the main treatment (in autumn), and the application dose was P40 (77 kg in physical weight per 1 ha). For wheat, a nitrogen–phosphorus–potassium fertilizer (NPK 16:16:10) was used for the main treatment (in autumn), and the application dose was N40P40K40; in spring, ammonium nitrate (N40) was applied on frozen soil. Details on the applied pesticides are provided in Table A2 (Appendix A).
The area of each plot was 20 × 12 m. Each treatment was carried out in triplicate. Soil samples were taken twice—during the growing season before applying pesticides, and after applying pesticides at the end of the growing season. Soil samples were collected from five different undisturbed locations at the depth of 0–20 cm for each site (“envelope method”). The samples were thoroughly mixed until homogeneous.

2.2. Bacteria Strains

A battery of whole-cell bacterial lux biosensors was used to assess the ecotoxicity of soil samples. The integral (general) toxicity of soil samples was determined using the natural strain of V. aquamarinus VKPM B-11245 [24]. The biotest is based on the principle of V. aquamarinus VKPM B-11245 bioluminescence “suppression” in the presence of toxic substances. To determine genotoxicity, biosensors with inducible luminescence of E. coli MG1655 (pRecA-lux), E. coli MG1655 (pDinB-lux) and E. coli MG1655 (pAlkA-lux) were used. PrecA, PdinB, and PalkA promoters are induced by DNA damage in the plasmids of these strains. Biosensors E. coli MG1655 (pKatG-lux), E. coli MG1655 (pSoxS-lux), E. coli MG1655 (pOxyR-lux), the promoters of which (PkatG, PsoxS and PoxyR) respond to the presence of pro-oxidant substances [25], were used to detect the substances that induce oxidative stress in cells (hydrogen peroxide (H2O2), organic peroxides, superoxide radical ion (O2−). E. coli strain MG1655 (pGrpE-lux), containing the “heat shock” promoter PgrpE, was used as a specific biosensor for toxicants that damage cellular proteins. E. coli strain MG1655 (pFabA-lux) was used to determine the toxicity of media containing chemicals and cell-membrane-modifying materials. The strain contains the fabA gene promoter, which encodes the ß-hydroxydecanoyl-[acyl-carrier-protein] dehydratase enzyme, a key protein in the synthesis of unsaturated fatty acids (components of cell membranes) [26]. In order to correct artifacts associated with changes in bacterial luciferase activity and not associated with induction, a genetically engineered E. coli MG1655 (pXen7) strain with a constitutive promoter was used [25]. All biosensors with inducible luminescence contained hybrid plasmids based on the pBR322 vector, carrying the Photorhabdus luminescens luxCDABE gene cassette under the control of the corresponding promoters and a selective marker for ampicillin resistance.

2.3. Assessment of Soil Ecotoxicity Using Bacterial Lux-Biosensors

Soil extracts were prepared according to the protocol described in the article by Sazykina [15]. Cells of bacterial strains were incubated in Luria-Bertani (LB) medium [27] containing 100 μg of ampicillin/mL with constant shaking for 18–20 h at 37 °C. V. aquamarinus VKPM B-11245 strain was grown without ampicillin until the early exponential phase at 25 °C. Bacterial strains were immediately used for stress induction tests.
The biosensors’ luminescence was measured on a Luminoskan Ascent microplate luminometer (Thermo Fisher Scientific, Waltham, MA, USA) for 120 min in three independent replicates. To carry this out, 20 μL of extracts of the studied soils was added to the wells of a 96-well microplate containing 180 μL of culture. Then, 20 μL of distilled water were added instead of soil extracts into the wells with negative control; 20 μL of a toxicant solution activating the corresponding promoter was added into the wells with the positive control. Numerical values of bioluminescence were expressed in relative luminescence units.
To assess the toxic effect of substances contained in soil extracts, the induction coefficient Fi was calculated, defined as the ratio of luminescence intensity of lux-biosensor suspension containing the test sample (Lc) to luminescence intensity of the control lux-biosensor suspension (Lk): Fi = Lc/Lk. Since natural substrates contain substances that can both suppress and stimulate the activity of bacterial luciferase itself, the E. coli strain MG1655 (pXen7), in which the lux operon is under the control of a constitutive promoter, was also used.
For this strain, the coefficient of inductive suppression of luminescence (K) was calculated, K = lc/lk, where lc is luminescence intensity of the suspension of the strain with a constitutive promoter in the presence of the test compound; lk represents the luminescence intensity of the lux-strain with a constitutive promoter control suspension. The true values of the induction factor (I) were calculated using the formula I = Fi/K, where Fi is the induction coefficient, and K is the luminescence suppression coefficient. The degree of toxicity for genetically engineered E. coli strains using I values was assessed as follows: mild toxicity (I  <  2), moderate toxicity (2  ≤  I  ≤  10), and severe toxicity (I  >  10) [28].
When determining the integral toxicity of soils using the natural V. aquamarinus VKPM B-11245 strain, the toxicity index (T) was calculated as T = 100 (Ik − Ic)/Ic, where Ic and Ik are the luminescence intensity of bacteria in the test and control samples, respectively, at a fixed exposure time of bacteria (30 min) with soil extracts. Soil samples were considered admissibly toxic when I  <  20, toxic when 20  ≤  I  <  50, and highly toxic when I  >  50 [29]. The experiments were performed in three independent replicates.

2.4. Statistical Processing

Statistical processing of the results was carried out using standard methods of mathematical statistics. Statistical analysis was performed using GraphPad Prism 8.0.2 DEMO (GraphPad Software, Inc., San Diego, CA, USA) using two-way ANOVA and t-test (p ≤ 0.05). For visualization, we used package “seaborn” for Python 3.8.

3. Results

3.1. Integral Toxicity Determined Using the V. aquamarinus VKPM B-11245 Strain

The study of integral toxicity of soil samples under soya crops showed an acceptable degree of toxicity for almost all samples, except for the soil treated with both pesticides and fertilizers (T = 29.05 ± 4.30, average toxicity). At the same time, soil under sunflower crops, taken at the end of the growing season, had significant integral toxicity in all treatment options, and the highest with the combined treatment (T = 71.88 ± 5.80) (Figure 1, Table A3 (Appendix A)). In soil under wheat grown after soya, the initial toxicity of all samples was admissible; however, agrochemical treatments in all variants contributed to a significant increase in toxicity—all samples were characterized as highly toxic (T = 64.29 ± 4.00; T = 59.87 ± 2.00; T = 76.87 ± 3.00, Figure 1). Soil under wheat crops after sunflower had admissible initial toxicity (except for the soil with combined treatment, where T = 35.29 ± 0.90). The application of pesticides slightly increased the integral toxicity of the soil (T = 27.34 ± 2.00). For soil with the combined treatment, a decrease in toxicity was noted during the cultivation of wheat (T = 23.8 ± 3.00). The soil with the application of mineral fertilizers was highly toxic (T = 86.34 ± 6.00).

3.2. Genotoxicity of Agricultural Soils

The response of the E. coli MG1655 (pRecA-lux) strain, which detects the presence of DNA-damaging substances, revealed the negative impact of applying pesticides separately and as part of the combined treatment of soil under soya and sunflowers (Figure 2, Table A3). During the subsequent cultivation of wheat after soya, no significant biosensor response was recorded either in the initial sampling or after treatments. At the same time, with the help of this biosensor, it was possible to register the negative effects that persisted after treating the predecessor of wheat (sunflower) with fertilizers and pesticides. Interestingly, at the end of the wheat growing season, genotoxicity could no longer be detected using the E. coli MG1655 (pRecA-lux) biosensor.
Using the E. coli MG1655 (pDinB-lux) strain (detects genotoxicants that trigger SOS response in the cell), a weak toxic effect of the combined soil treatment under sunflower crops (Figure 2, Table A3) of all variants of agrochemical soil treatments under wheat crops after soya was revealed, and that of introducing fertilizers and pesticides separately into the soil under wheat after sunflower.
The genotoxic effects of fertilizers and pesticides applied jointly to soya crops were noted using the E. coli MG1655 (pColD-lux) biosensor (I = 1.81 ± 0.27) (Figure 2, Table A3). For almost all treatments in soil under sunflower, a negative effect was found due to the presence of genotoxicants, which persisted during the subsequent cultivation of wheat, increasing towards the end of the growing season. Genotoxicity due to the presence of alkylating agents was detected using the E. coli MG1655 (pAlkA-lux) strain in all soil treatments for soya, sunflower and wheat after both predecessors (Figure 2, Table A3).

3.3. Pro-oxidant Properties of Agricultural Soils

Using biosensor strains that detect the presence of substances generating oxidative stress (E. coli MG 1655 (pKatG-lux), E. coli MG 1655 (pOxyR-lux), E. coli MG 1655 (pSoxS-lux)), no significant pro-oxidant effect of all variants of soil treatments under soya was found, except for one sample (E. coli MG 1655 (pOxyR-lux) sensor, soil with pesticides, I = 1.64 ± 0.03) (Figure 3, Table A3). In soil under sunflower, the response of the E. coli MG 1655 (pKatG-lux) sensor to pesticide treatment (I = 1.57 ± 0.04), and that of the E. coli MG 1655 (pSoxS-lux) sensor to a combined treatment (I = 1.59 ± 0.04) were registered. There was a much stronger oxidative stress, caused by the presence of substances that generate superoxide radicals in soil under wheat crops after both predecessors—the response of the E. coli MG 1655 (pSoxS-lux) sensor was recorded in all variants of agrochemical treatments. In addition, weak toxicity due to peroxides (E. coli MG 1655 (pOxyR-lux) sensor) was observed when fertilizers and pesticides were applied separately to soil under wheat after sunflower.

3.4. Protein- and Membrane-Damaging Properties of Soils

All variants of soil treatments under soya and sunflower did not lead to an increase in the protein-damaging properties of the studied soils extracts, detected using the sensor E. coli MG1655 (pGrpE-lux) (Figure 4, Table A3). However, when growing wheat, the emergence of soil toxic properties due to protein damage was recorded in all variants of agrochemical treatments (except for soil after sunflower with mineral fertilizers application). Weak toxicity due to the presence of cell-membrane-damaging substances was demonstrated using the E. coli MG1655 (pFabA-lux) strain when pesticides were applied (separately and together with fertilizers) to soya crops (Figure 4, Table A3). All variants of soil treatments under sunflower led to an increase in the membrane-damaging properties of soils (from weak to medium levels). Regarding further wheat growth after soya, the toxic effect was recorded at the end of the growing season with the combined treatment, and when grown after sunflower, it was found in the initial soil samples in all treatment options. By the end of the wheat growing season, membrane-damaging properties were recorded only in the soil with the pesticides’ addition.

4. Discussion

Soil is a complex matrix in which living components, chemical and physical factors interact. When studying the influence of agrochemical treatments on agricultural soils’ ecotoxicity, we are dealing not only with the toxicity of individual chemicals introduced into the soil, but also with the result of complex interactions of chemicals, microorganisms, plant root exudates, and physical and chemical soil parameters. A number of studies have shown that, indeed, the toxicity or environmental risk of pollutants in soil is influenced not only by their total amount or availability, but also by physicochemical soil properties, pH value, environmental properties, and the ionic strength of contaminated areas [30].
In this work, a synergistic negative effect of pesticides and fertilizers when applied to plant crops was discovered using the natural bioluminescent strain V. aquamarinus VKPM B-11245, the operating principle of which is based on suppressing the strain luminescence in the presence of a sum of toxicants. Similar results were shown by Yang et al. [31], who used biosensors to find both synergistic and antagonistic effects of the combination of heavy metals and pesticides on soil cytotoxicity. The bioavailability of pollutants, not just their direct concentration in environmental samples, is also important. This was shown by Sazykin et al. [32] when studying the ecotoxicity of river Don bottom sediments using bacterial lux biosensors, as well as by Azhogina et al. [18], who found a close connection between the response of biosensors and the concentration of bioavailable PAHs.
Integral toxicity. In this work, we also observed an indirect effect of agrochemical treatments on the response of the V. aquamarinus VKPM B-11245 strain, which was especially pronounced in the soil under wheat crops. At the same dosages of agrochemicals, the integral toxicity of soil after growing soya and after growing sunflowers changed significantly. The highest toxicity was found in the soil under wheat after sunflower with mineral fertilizers’ application. At the same time, a response of comparable strength from other biosensors for this sample was not found. This may indicate other mechanisms of toxicity that are not detected by the biosensors used. In addition, a negative effect can be realized not through the direct action of a chemical, but through its influence on plants and soil microbiome.
Genotoxicity. Using several sensors that respond to the presence of DNA-damaging substances, the genotoxic properties of soil with pesticides application (both separately and in combination with mineral fertilizers) were discovered. The biosensors set was formed in such a way as to cover the main mechanisms of genotoxic effects (DNA damage that blocks replication and induces an SOS response, as well as DNA alkylation, which does not always stop the replication fork and does not cause SOS response induction). The strongest reaction to agrochemical treatments was shown by the E. coli MG1655 (pAlkA-lux) strain, which detects alkylating agents. This corresponds to the evidence that a number of pesticides have alkylating properties and can react with nucleophilic regions of DNA, causing genotoxicity [33]. The E. coli MG1655 (pRecA-lux) strain response to the application of pesticides separately and as a part of combined application to soil under soya and sunflower was also recorded. The genotoxic effects of pesticide residues in contaminated soil were also shown by Zeyad et al. [34]. Using prokaryotic tests (in particular, E. coli K-12 mutants with a DNA repair defect), it was found that survival of polA, lexA and recA mutants was 39%, 47% and 55% when treated with hexane extract of contaminated soil. The same was previously shown for soils irrigated with wastewater from pesticide production plants—the survival of E. coli K-12 mutants with DNA repair defect decreased when exposed to soil extracts [35].
When studying genotoxicity of total petroleum hydrocarbons in contaminated soils and groundwater using the biosensor strain Acinetobacter baylyi ADPWH_recA, a high level of genotoxicity was found in soil and groundwater samples with lower concentrations of TPH (4338.0 mg/kg and 1.4 mg/L mitomycin C equivalent). This may indicate a significant influence of geochemical variables and alkanes availability on ecological risks of oil pollution [13]. We believe that this assumption is also valid for agricultural soils contaminated with agrochemicals. In addition, introduced substances can increase soil genotoxicity due to reaction of soil organisms and plants. Using the E. coli MG1655 (pRecA-lux) strain, it was possible to register residual genotoxicity during further cultivation of wheat after sunflower. This can be explained by a longer growing season of sunflower and the need to apply more pesticides and use desiccants. Soya harvest is completed early, and, apparently, soil has enough time to recover before the wheat growing season.
The ability of whole-cell bacterial biosensors with the recA promoter to detect changes in genotoxicity has been well demonstrated in oil-contaminated seawater. Using the E. coli RecA::luxCDAB biosensor strain, Jiang et al. [14] found a spatial and temporal variation in genotoxicity of seawater contaminated with crude oil, most likely due to crude oil degradation process. In earlier work, using another biosensor strain, Acinetobacter ADPWH_recA, it was also possible to record a decrease in alkane content and genotoxicity to the detection threshold [21].
However, as the results of the present study show, it is important to use a combination of biosensor strains. This is clearly seen in the example of soil under wheat, where at the end of the cultivation period the E. coli MG1655 (pRecA-lux) strain no longer detects genotoxicity, but other sensors, especially E. coli MG1655 (pAlkA-lux), reacting to the presence of alkylating agents, indicate the presence of genotoxic properties of the soil. Apparently, over time, partial degradation of pesticides in soil occurs under the influence of temperature, humidity, pH, etc., forming partial decomposition products that are more or less toxic than the original compounds.
They may no longer cause structural disturbances of the bacterial genome, including single- and double-strand DNA breaks, which leads to a decrease in the induction of the biosensor strain E. coli MG1655 (pRecA-lux), which responds to the expression of the SOS response.
Oxidative stress. Using biosensor strains that detect oxidative stress, it was found that agrochemical treatments (in all variants) increase oxidative stress caused by the presence of superoxide anion in the medium. It is known that pesticides can act as potent inducers of oxidative stress, which results from an imbalance between reactive oxygen species (ROS) and antioxidant mechanisms [36]. In a recent study, Sazykin et al. [37] showed that glyphosate pesticide caused oxidative stress due to increased levels of superoxide and peroxide (determined using E. coli MG 1655 (pSoxS-lux) and E. coli MG 1655 (pKatG-lux) strains), and increased the level of mutagenesis in E. coli. Similar results were obtained in the work of Yang et al. [31], where, using the biosensor strain Acinetobacter ADPWH_recA, it was shown that combined exposure of soil to Ag(I), Cr(VI) and four pesticides (dichlorvos, parathion, omethoate, monocrotophos) aggravated oxidative damage associated with ROS compared with individual pollutants. For other organisms (mammals, plants), there is ample evidence of the ability of pesticides to induce oxidative stress and generate ROS, especially when interacting with heavy metals [38,39,40,41]. Oxidative stress in the cell triggers a cascade of protective reactions, including the SOS response, accompanied by DNA damage. In this work, an increase in the genotoxicity of treated soils was detected using lux biosensors, which suggests a mechanism of toxic action of agrochemicals due to oxidative DNA damage. Thus, the data on oxidative stress in soils treated with agrochemicals, obtained using bacterial lux biosensors, are consistent with the results observed for other objects. We believe that this tool can be used to quickly and inexpensively assess oxidative stress caused by various environmental pollutants.
Damage to proteins and membranes. Soil under wheat after both predecessors had protein-damaging properties. The response of the E. coli MG1655 (pGrpE-lux) sensor in these soils looks quite natural, given that in these samples, with the help of other strains, both an increased level of oxidative stress and genotoxicity due to DNA alkylation were observed. Interestingly, in this work, we did not observe a coordinated response of sensors to protein damage and membrane damage. Using the E. coli MG1655 (pFabA-lux) strain, toxic effects of both pesticides and fertilizers on cell membranes in the soil under soya and sunflowers were detected, while the E. coli MG1655 (pGrpE-lux) strain did not show any negative effects here.
Membrane damage was recorded both with individual and combined application of chemicals, while, for example, with the combined application of Ag(I), Cr(VI) and four pesticides in the work of Yang [31], membrane damage was not observed in bioreporter cells in response to mixtures of heavy metals and pesticides. Summarizing the responses of various biosensors, it can be assumed that the application of agrochemicals to the soil increases oxidative stress caused by superoxide level, which leads to triggering an SOS response, accompanied by damage to DNA, cell membranes and proteins.

5. Conclusions

A battery of whole-cell bacterial lux-biosensors based on the E. coli MG1655 strain with inducible promoters can be used to monitor the ecotoxicity of natural environments, in particular that of agricultural soils. Using biosensors, it is possible to assess the total impact of pollutants on living organisms and biological mechanisms that mediate toxicity. It has been shown that the application of pesticides and fertilizers to plant crops increases the overall soil toxicity, which may be due to a damaging effect on DNA, proteins and membranes, as well as an increase in the level of superoxide anion radical.

Author Contributions

Conceptualization, L.K. and I.S.; data curation, T.A.; formal analysis, T.A., M.K. (Maria Klimova), and S.K.; funding acquisition, L.K.; investigation, S.K., M.K., A.L., E.P. and E.C.; methodology, I.S.; project administration, E.P., L.K. and I.S.; software, T.A.; supervision, M.S. and E.P.; validation, M.K., S.K. and T.A.; visualization, A.L. and E.C.; writing—original draft preparation, L.K., M.K. and S.K.; writing—review and editing, M.K. (Margarita Khammami), M.K. (Maria Klimova), S.K., E.P., A.L., E.C. and M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Russian Science Foundation, grant number 21-76-10048, https://rscf.ru/en/project/21-76-10048/ (accessed on 10 April 2024), at Southern Federal University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data supporting the results of this study are available within this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

soya crops (G), sunflower (H), wheat grown after soya (T(g)) and after sunflower T(h), c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.

Appendix A

Table A1. Soil samples collected during a field experiment.
Table A1. Soil samples collected during a field experiment.
№DesignationCropAgrochemical TreatmentSampling TimeForecrop
Sampling before pesticide application
1Gcsoyacontrol14.06.2022–
2Gfsoyafertilizers14.06.2022–
3Gpsoyapesticides14.06.2022–
4Gf + psoyafertilizers + pesticides14.06.2022–
5Hcsunflowercontrol14.06.2022–
6Hfsunflowerfertilizers14.06.2022–
7Hpsunflowerpesticides14.06.2022–
8Hf + psunflowerfertilizers + pesticides14.06.2022–
9T(g)cwinter wheatcontrol15.05.2023soya
10T(g)fwinter wheatfertilizers15.05.2023soya
11T(g)pwinter wheatpesticides15.05.2023soya
12T(g)f + pwinter wheatfertilizers + pesticides15.05.2023soya
13T(h)cwinter wheatcontrol15.05.2023sunflower
14T(h)fwinter wheatfertilizers15.05.2023sunflower
15T(h)pwinter wheatpesticides15.05.2023sunflower
16T(h)f + pwinter wheatfertilizers + pesticides15.05.2023sunflower
Sampling after pesticide application
17Gcsoyacontrol07.07.2022–
18Gfsoyafertilizers07.07.2022–
19Gpsoyapesticides07.07.2022–
20Gf + psoyafertilizers + pesticides07.07.2022–
21Hcsunflowercontrol22.09.2022–
22Hfsunflowerfertilizers22.09.2022–
23Hpsunflowerpesticides22.09.2022–
24Hf + psunflowerfertilizers + pesticides22.09.2022–
25T(g)cwinter wheatcontrol04.07.2023soya
26T(g)fwinter wheatfertilizers04.07.2023soya
27T(g)pwinter wheatpesticides04.07.2023soya
28T(g)f + pwinter wheatfertilizers + pesticides04.07.2023soya
29T(h)cwinter wheatcontrol04.07.2023sunflower
30T(h)fwinter wheatfertilizers04.07.2023sunflower
31T(h)pwinter wheatpesticides04.07.2023sunflower
32T(h)f + pwinter wheatfertilizers + pesticides04.07.2023sunflower
Table A2. Chemical plant protection products used in this study.
Table A2. Chemical plant protection products used in this study.
Plant-Protecting
Agent		Trade NameCompositionApplicationDose
(L ha−1)		Treatment MethodCrop
HerbicidesGardo Gold312.5 g L−1 c-metolachlor
187.5 g L−1 terbutylazine		SE4.0 application to the soil before sowingsoya
3.0 sunflower
Benito300 g L−1 bentazoneCC2.0 during vegetationsoya
Reglon Super150 g L−1 diquatWS2.0 before harvesting (desiccant)sunflower
FungicidesMaxim25 g L−1 fludioxonilSC5.0 pre-sowing seed treatment (protectant)sunflower
Optimo200 g L−1 pyraclostrobinEC1.0 during growing seasonsunflower
Ceriax Plus66.6 g L−1 pyraclostrobin +
41.6 g L−1 fluxapyroxad +
41.6 g L−1 epoxiconazole		EC0.4 during growing seasonwinter wheat
InsecticidesCruiser350 g L−1 thiamethoxamSC0.5pre-sowing seed treatment
(seed dresser)		sunflower
Ampligo50 g L−1 lambda-cyhalothrin;
100 g L−1 chlorantraniliprole		MS0.2during growing seasonsunflower
Fascord100 g L−1 alpha-cypermethrinEC0.15during growing seasonwinter wheat
Notes: SE is suspension emulsion, CC are colloidal concentrates, WS is water solution, SC are suspension concentrates, EC are emulsion concentrates, MS is microencapsulated suspension.
Table A3. Variation in toxicity of the studied soils under different experimental conditions.
Table A3. Variation in toxicity of the studied soils under different experimental conditions.
CropSampling TimeAbbreviationResponse of Lux-Biosensor Strains
V. aquamarinus VKPM B-11245E. coli MG1655 (pRecA-lux)E. coli MG1655 (pDinB-lux)E. coli MG1655 (pColD-lux)E. coli MG1655 (pAlkA-lux)E. coli MG1655 (pKatG-lux)E. coli MG1655 (pOxyR-lux)E. coli MG1655 (pSoxS-lux) E. coli MG1655 (pGrpE-lux)E. coli MG1655 (pFabA-lux)
SoyabeforeGc12.60 ± 1.201.24 ± 0.041.05 ± 0.031.14 ± 0.051.5 ± 0.181.16 ± 0.011.14 ± 0.081.12 ± 0.011.09 ± 0.031.05 ± 0.04
Gf11.50 ± 0.901.22 ± 0.021.03 ± 0.031.13 ± 0.201.41 ± 0.121.13 ± 0.041.11 ± 0.041.15 ± 0.071.09 ± 0.051.07 ± 0.02
Gp13.45 ± 2.001.48 ± 0.061.31 ± 0.021.19 ± 0.031.13 ± 0.041.16 ± 0.031.28 ± 0.051.32 ± 0.061.09 ± 0.041.32 ± 0.02
Gf + p10.05 ± 0.401.31 ± 0.071.28 ± 0.121.3 ± 0.101.16 ± 0.041.11 ± 0.081.27 ± 0.111.21 ± 0.091.12 ± 0.081.33 ± 0.10
afterGc15.11 ± 1.101.19 ± 0.041.13 ± 0.021.25 ± 0.051.3 ± 0.021.17 ± 0.101.3 ± 0.031.19 ± 0.131.12 ± 0.051.17 ± 0.08
Gf18.40 * ± 2.501.25 ± 0.061.17 ± 0.081.32 ± 0.201.64 * ± 0.131.11 ± 0.041.38 ± 0.041.25 ± 0.061.13 ± 0.011.26 ± 0.11
Gp17.24 * ± 1.201.95 * ± 0.111.38 ± 0.071.26 ± 0.062.53 * ± 0.061.47 ± 0.131.64 * ± 0.031.29 ± 0.061.1 ± 0.051.76 * ± 0.19
Gf + p29.05 * ± 4.301.76 * ± 0.191.27 ± 0.091.81 * ± 0.272.47 * ± 0.461.43 ± 0.121.48 ± 0.121.35 ± 0.101.26 ± 0.131.78 * ± 0.18
SunflowerbeforeHc15.6 ± 2.001.43 ± 0.051.28 ± 0.111.09 ± 0.041.01 ± 0.031.27 ± 0.051.12 ± 0.091.39 ± 0.121.26 ± 0.031.16 ± 0.01
Hf10.7 ± 1.501.37 ± 0.121.34 ± 0.051.45 ± 0.161.13 ± 0.041.18 ± 0.131.23 ± 0.031.17 ± 0.061.09 ± 0.071.28 ± 0.09
Hp11.2 ± 2.301.4 ± 0.071.04 ± 0.011.28 ± 0.061.02 ± 0.031.26 ± 0.061.02 ± 0.021.36 ± 0.121.05 ± 0.021.23 ± 0.02
Hf + p10.9 ± 0.801.39 ± 0.121.11 ± 0.091.4 ± 0.051.17 ± 0.051.06 ± 0.121.17 ± 0.031.09 ± 0.041.18 ± 0.061.17 ± 0.03
afterHc17.05 ± 2.001.17 ± 0.011.34 ± 0.041.44 ± 0.11.12 ± 0.11.33 ± 0.021.26 ± 0.021.29 ± 0.041.14 ± 0.021.21 ± 0.08
Hf35.1 *1 ± 5.001.47 ± 0.041.40 ± 0.071.48 ± 0.091.94 * ± 0.311.36 ± 0.031.33 ± 0.041.47 ± 0.041.05 ± 0.061.79 * ± 0.06
Hp25.88 * ± 4.101.96 * ± 0.031.48 ± 0.032.11 * ± 0.072.59 * ± 0.071.57 * ± 0.041.28 ± 0.021.37 ± 0.041.29 ± 0.071.47 ± 0.07
Hf + p71.88 * ± 5.801.55 * ± 0.061.91 * ± 0.021.87 * ± 0.112.60 * ± 0.141.24 ± 0.071.21 ± 0.021.59 * ± 0.041.5 ± 0.041.65 * ± 0.06
Wheat grown after soyabeforeT(g)c6.72 ± 0.601.41 ± 0.021.15 ± 0.021.27 ± 0.081.29 ± 0.10.98 ± 0.011.35 ± 0.021.13 ± 0.061.29 ± 0.071.1 ± 0.05
T(g)f9.54 ± 0.071.39 ± 0.041.18 ± 0.181.49 ± 0.162.1 * ± 0.461.07 ± 0.031.38 ± 0.031.12 ± 0.151.26 ± 0.041.28 ± 0.05
T(g)p4.36 ± 0.041.41 ± 0.101.28 ± 0.041.52 * ± 0.031.85 * ± 0.421.26 ± 0.081.45 ± 0.041.21 ± 0.031.27 ± 0.091.34 ± 0.06
T(g)f + p8.76 ± 0.111.41 ± 0.061.19 ± 0.071.4 ± 0.122.15 * ± 0.131.11 ± 0.041.39 ± 0.051.15 ± 0.051.34 ± 0.051.29 ± 0.05
afterT(g)c15.8 * ± 1.001.14 ± 0.061.43 ± 0.061.31 ± 0.031.73 * ± 0.571.03 ± 0.061.11 ± 0.041.13 ± 0.061.07 ± 0.041.14 ± 0.02
T(g)f64.29 * ± 4.001.28 ± 0.051.58 * ± 0.061.58 * ± 0.074.28 * ± 0.131.1 ± 0.041.16 ± 0.062.11 * ± 0.101.56 * ± 0.121.48 ± 0.08
T(g)p59.87 * ± 2.001.23 ± 0.051.88 * ± 0.061.81 * ± 0.132.93 * ± 0.661.27 ± 0.041.32 ± 0.072.46 * ± 0.091.94 * ± 0.081.45 ± 0.18
T(g)f + p76.87 * ± 3.001.10 ± 0.031.58 * ± 0.081.64 * ± 0.114.88 * ± 0.921.16 ± 0.051.31 ± 0.112.19 * ± 0.131.66 * ± 0.091.53 * ± 0.15
Wheat grown after sunflowerbeforeT(h)c19.24 * ± 0.101.48 ± 0.051.3 ± 0.051.49 ± 0.061.58 * ± 0.171.11 ± 0.051.45 ± 0.041.19 ± 0.041.37 ± 0.091.42 ± 0.06
T(h)f20.23 * ± 0.301.62 * ± 0.071.25 ± 0.061.54 * ± 0.122.17 * ± 0.191.19 ± 0.061.47 ± 0.051.3 ± 0.081.42 ± 0.101.63 * ± 0.06
T(h)p14.55 * ± 0.101.62 * ± 0.071.24 ± 0.061.52 * ± 0.161.18 ± 0.061.2 ± 0.111.32 ± 0.061.37 ± 0.041.25 ± 0.111.86 * ± 0.08
T(h)f + p35.29 * ± 0.901.45 ± 0.071.2 ± 0.091.78 * ± 0.142.05 * ± 0.161.17 ± 0.091.37 ± 0.071.3 ± 0.051.6 * ± 0.071.64 * ± 0.14
afterT(h)c20.65 * ± 2.001.12 ± 0.031.49 ± 0.081.5 ± 0.101.8 * ± 0.271.21 ± 0.071.51 * ± 0.031.44 ± 0.091.28 ± 0.081.34 ± 0.18
T(h)f86.34 * ± 6.001.34 ± 0.081.53 * ± 0.091.52 * ± 0.092.75 * ± 0.231.31 ± 0.061.54 * ± 0.091.63 * ± 0.111.29 ± 0.051.42 ± 0.05
T(h)p27.34 * ± 2.001.34 ± 0.031.71 * ± 0.022.08 * ± 0.122.5 * ± 0.571.28 ± 0.021.54 * ± 0.041.66 * ± 0.041.63 * ± 0.021.67 * ± 0.03
T(h)f + p23.8 * ± 3.001.04 ± 0.061.40 ± 0.051.88 * ± 0.143.95 * ± 0.201.32 ± 0.071.43 ± 0.101.88 * ± 0.091.51 * ± 0.061.37 ± 0.07
* Differences compared to the control samples are statistically significant. The values were expressed as mean ± SD. Student’s t-test was used to compare these values. Values of p lower than 0.05 were considered significant. Each experiment was performed in triplicate and repeated on three different occasions.

References

  1. Negatu, B.; Kromhout, H.; Mekonnen, Y.; Vermeulen, R. Use of Chemical Pesticides in Ethiopia: A Cross-Sectional Comparative Study on Knowledge, Attitude and Practice of Farmers and Farm Workers in Three Farming Systems. Ann. Occup. Hyg. 2016, 60, 551–566. [Google Scholar] [CrossRef] [PubMed]
  2. Asghar, U.; Malik, M.F.; Javed, A. Pesticide exposure and human health: Review. J. Ecosys. Ecograph. 2016, s5, 1–2. [Google Scholar] [CrossRef]
  3. Chai, M.K.; Tan, G.H. Validation of a headspace solid-phase microextraction procedure with gas chromatography-electron capture detection of pesticide residues in fruits and vegetables. Food Chem. 2009, 117, 561–567. [Google Scholar] [CrossRef]
  4. Huo, F.; Tang, H.; Wu, X.; Chen, D.; Zhao, T.; Liu, P.; Li, L. Utilizing a novel sorbent in the solid phase extraction for simultaneous determination of 15 pesticide residues in green tea by GC/MS. J. Chromatogr. B. 2016, 1023–1024, 44–54. [Google Scholar] [CrossRef] [PubMed]
  5. Singh, R.; Kumar, N.; Mehra, R.; Kumar, H.; Singh, V.P. Progress and challenges in the detection of residual pesticides using nanotechnology based colorimetric techniques. Trends Environ. Anal. Chem. 2020, 26, e00086. [Google Scholar] [CrossRef]
  6. Yagi, K. Applications of whole-cell bacterial sensors in biotechnology and environmental science. Appl. Microbiol. Biotechnol. 2007, 73, 1251–1258. [Google Scholar] [CrossRef] [PubMed]
  7. Zhang, D.Y.; Zhao, Y.; He, Y.; Wang, Y.; Zhao, Y.Y.; Zheng, Y.; Wei, X.; Zhang, L.T.; Li, Y.Z.; Jin, T.; et al. Characterization and modeling of transcriptional cross-regulation in Acinetobacter baylyi ADP1. ACS Synth. Biol. 2012, 1, 274–283. [Google Scholar] [CrossRef] [PubMed]
  8. Belkin, S. Microbial whole-cell sensing systems of environmental pollutants. Curr. Opin. Microbiol. 2003, 6, 206–212. [Google Scholar] [CrossRef]
  9. Su, L.; Jia, W.; Hou, C.; Lei, Y. Microbial biosensors: A review. Biosens. Bioelectron. 2011, 26, 1788–1799. [Google Scholar] [CrossRef]
  10. Rampley, C.P.N.; Whitehead, P.G.; Softley, L.; Hossain, M.A.; Jin, L.; David, J.; Shawal, S.; Das, P.; Thompson, I.P.; Huang, W.E.; et al. River toxicity assessment using molecular biosensors: Heavy metal contamination in the Turag-Balu-Buriganga river systems, Dhaka, Bangladesh. Sci. Total Environ. 2020, 703, 134760. [Google Scholar] [CrossRef]
  11. Cui, Z.; Luan, X.; Jiang, H.; Li, Q.; Xu, G.; Sun, C.; Zheng, L.; Song, Y.; Davison, P.A.; Huang, W.E. Application of a bacterial whole cell biosensor for the rapid detection of cytotoxicity in heavy metal contaminated seawater. Chemosphere. 2018, 200, 322–329. [Google Scholar] [CrossRef] [PubMed]
  12. Song, Y.; Li, G.; Thornton, S.F.; Thompson, I.P.; Banwart, S.A.; Lerner, D.N.; Huang, W.E. Optimization of Bacterial Whole Cell Bioreporters for Toxicity Assay of Environmental Samples. Environ. Sci. Technol. 2009, 43, 7931–7938. [Google Scholar] [CrossRef] [PubMed]
  13. Liu, Y.; Zhao, X.; Wang, X.; Ding, A.; Zhang, D. Application of whole-cell bioreporters for ecological risk assessment and bioremediation potential evaluation after a benzene exceedance accident in groundwater in Lanzhou, China. Sci. Total Environ. 2024, 906, 167846. [Google Scholar] [CrossRef] [PubMed]
  14. Jiang, B.; Li, G.; Xing, Y.; Zhang, D.; Jia, J.; Cui, Z.; Luan, X.; Tang, H. A whole-cell bioreporter assay for quantitative genotoxicity evaluation of environmental samples. Chemosphere 2017, 184, 384–392. [Google Scholar] [CrossRef] [PubMed]
  15. Sazykina, M.A.; Chistyakov, V.A.; Sazykin, I.S. Genotoxicity of Don River Bottom Sediments (2001–2007). Water Res. 2012, 39, 118–124. [Google Scholar] [CrossRef]
  16. Sazykin, I.S.; Sazykina, M.A.; Khmelevtsova, L.E.; Mirina, E.A.; Kudeevskaya, E.M.; Rogulin, E.A.; Rakin, A.V. Biosensor-based comparison of the ecotoxicological contamination of the wastewaters of Southern Russia and Southern Germany. Int. J. Environ. Sci. Technol. 2016, 13, 945–954. [Google Scholar] [CrossRef]
  17. Sazykina, M.; Barabashin, T.; Konstantinova, E.; Al-Rammahi, A.A.K.; Pavlenko, L.; Khmelevtsova, L.; Karchava, S.; Klimova, M.; Mkhitaryan, I.; Khammami, M.; et al. Non-corresponding contaminants in marine surface sediments as a factor of ARGs spread in the Sea of Azov. Mar. Pollut. Bull. 2022, 184, 114196. [Google Scholar] [CrossRef] [PubMed]
  18. Azhogina, T.; Sazykina, M.; Konstantinova, E.; Khmelevtsova, L.; Minkina, T.; Antonenko, E.; Sushkova, S.; Khammami, M.; Mandzhieva, S.; Sazykin, I. Bioaccessible PAH influence on distribution of antibiotic resistance genes and soil toxicity of different types of land use. Environ. Sci. Pollut. Res. 2023, 30, 12695–12713. [Google Scholar] [CrossRef] [PubMed]
  19. Bazhenov, S.V.; Novoyatlova, U.S.; Scheglova, E.S.; Prazdnova, E.V.; Mazanko, M.S.; Kessenikh, A.G.; Kononchuk, O.V.; Gnuchikh, E.Y.; Liu, Y.; Al Ebrahim, R.; et al. Bacterial lux-biosensors: Constructing, applications, and prospects. Biosens. Bioelectron. X 2023, 13, 100323. [Google Scholar] [CrossRef]
  20. Song, Y.; Jiang, B.; Tian, S.; Tang, H.; Liu, Z.; Li, C.; Jia, J.; Huang, W.E.; Zhang, X.; Li, G. A whole-cell bioreporter approach for the genotoxicity assessment of bioavailability of toxic compounds in contaminated soil in China. Environ. Pollut. 2014, 195, 178–184. [Google Scholar] [CrossRef]
  21. Zhang, D.; Ding, A.; Cui, S.; Hu, C.; Thornton, S.F.; Dou, J.; Sun, Y.; Huang, W.E. Whole cell bioreporter application for rapid detection and evaluation of crude oil spill in seawater caused by Dalian oil tank explosion. Water Res. 2013, 47, 1191–1200. [Google Scholar] [CrossRef] [PubMed]
  22. Ma, Z.; Li, Y.; Lu, Z.; Pan, J.; Li, M. A novel biosensor-based method for the detection of p-nitrophenol in agricultural soil. Chemosphere 2023, 313, 137306. [Google Scholar] [CrossRef] [PubMed]
  23. Zhang, B.; Sun, G.; Zhu, Y.; Paton, G.I. Quantification of the bioreactive Hg fraction in Chinese soils using luminescence-based biosensors. Environ. Technol. Innov. 2016, 5, 267–276. [Google Scholar] [CrossRef]
  24. Sazykin, I.S.; Sazykina, M.A.; Kudeevskaya, E.M.; Sazykina, M.I. The Strain Vibrio Aquamarinus, a Method for Determining the Toxicity of Samples with It and Test Culture to determine the Toxicity of Samples. Patent RU 2534819, 10 December 2014. IPC C12N1/20, C12R1/63, C12Q1/02. Bulletin No. 34. [Google Scholar]
  25. Zavilgelsky, G.B.; Kotova, V.Y.; Manukhov, I.V. Action of 1,1–dimethylhydrazine on bacterial cells is determined by hydrogen peroxide. Mutat. Res. 2007, 634, 172–176. [Google Scholar] [CrossRef] [PubMed]
  26. Choi, S.; Gu, M. A whole cell bioluminescent biosensor for the detection of membrane-damaging toxicity. Biotechnol. Bioprocess. Eng. 1999, 4, 59–62. [Google Scholar] [CrossRef]
  27. Maniatis, T.; Fritsch, E.F.; Sambrook, J. Molecular Cloning: A Laboratory Manual; Cold Spring Harbor Laboratory: New York, NY, USA, 1982; ISBN 0879691360. [Google Scholar]
  28. Sazykina, M.A.; Chistyakov, V.A.; Voinova, N.V. Method to Detect Genotoxicity of Chemical Substances. Patent RU 2179581, 20 February 2002. IPC C12Q1/02, C12Q1/66. Bulletin No. 5. [Google Scholar]
  29. Determination of the Integral Soil Toxicity Using the Ecolum Biotest. Methodological Recommendations N 01.019-07 (Approved by Rospotrebnadzor, Moscow, 15 June 2007). Available online: https://docs.cntd.ru/document/1200058483 (accessed on 10 April 2024).
  30. Besser, H.; Redhaounia, B.; Bedoui, S.; Ayadi, Y.; Khelifi, F.; Hamed, Y. Geochemical, isotopic and statistical monitoring of groundwater quality: Assessment of the potential environmental impacts of the highly polluted CI water in Southwestern Tunisia. J. Afr. Earth Sci. 2019, 153, 144–155. [Google Scholar] [CrossRef]
  31. Yang, Q.; Li, G.; Jin, N.; Zhang, D. Synergistic/antagonistic toxicity characterization and source-apportionment of heavy metals and organophosphorus pesticides by the biospectroscopy-bioreporter-coupling approach. Sci. Total Environ. 2023, 905, 167057. [Google Scholar] [CrossRef]
  32. Sazykin, I.S.; Sazykina, M.A.; Khammami, M.I.; Kostina, N.V.; Khmelevtsova, L.E.; Trubnik, R.G. Distribution of polycyclic aromatic hydrocarbons in surface sediments of lower reaches of the Don River (Russia) and their ecotoxicologic assessment by bacterial lux-biosensors. Environ. Monit. Assess. 2015, 187, 277. [Google Scholar] [CrossRef] [PubMed]
  33. Cortés-Eslava, J.; Gómez-Arroyo, S.; Arenas-Huertero, F.; Flores-Maya, S.; Díaz-Hernández, M.E.; Calderón-Segura, M.E.; Valencia-Quintana, R.; Espinosa-Aguirre, J.J.; Villalobos-Pietrini, R. The role of plant metabolism in the mutagenic and cytotoxic effects of four organophosphorus insecticides in Salmonella typhimurium and in human cell lines. Chemosphere 2013, 92, 1117–1125. [Google Scholar] [CrossRef]
  34. Zeyad, M.T.; Khan, S.; Malik, A. Genotoxic hazard and oxidative stress induced by wastewater irrigated soil with special reference to pesticides and heavy metal pollution. Heliyon 2022, 8, e10534. [Google Scholar] [CrossRef]
  35. Anjum, R.; Krakat, N. Genotoxicity assessments of alluvial soil irrigated with wastewater from a pesticide manufacturing industry. Environ. Monit. Assess. 2015, 187, 638. [Google Scholar] [CrossRef] [PubMed]
  36. Mostafalou, S.; Abdollahi, M.; Eghbal, M.A.; Kouzehkonani, N.S. Protective effect of NAC against malathion-induced oxidative stress in freshly isolated rat hepatocytes. Adv. Pharm. Bull. 2012, 2, 79–88. [Google Scholar] [CrossRef] [PubMed]
  37. Sazykin, I.; Naumova, E.; Azhogina, T.; Klimova, M.; Karchava, S.; Khmelevtsova, L.; Chernyshenko, E.; Litsevich, A.; Khammami, M.; Sazykina, M. Glyphosate effect on biofilms formation, mutagenesis and stress response of E. coli. J. Hazard. Mater. 2024, 461, 132574. [Google Scholar] [CrossRef] [PubMed]
  38. Xie, H.; Cheng, Y.; Cai, Y.; Ren, T.; Zhang, B.; Chen, N.; Wang, J. A H2O2-specific fluorescent probe for evaluating oxidative stress in pesticides-treated cells, rice roots and zebrafish. J. Hazard. Mater. 2024, 465, 133426. [Google Scholar] [CrossRef] [PubMed]
  39. Liu, N.; Zhong, G.; Zhou, J.; Liu, Y.; Pang, Y.; Cai, H.; Wu, Z. Separate and combined effects of glyphosate and copper on growth and antioxidative enzymes in Salvinia natans (L.) All. Sci. Total Environ. 2019, 655, 1448–1456. [Google Scholar] [CrossRef] [PubMed]
  40. Odetti, L.M.; López González, E.C.; Siroski, P.A.; Simoniello, M.F.; Poletta, G.L. How the exposure to environmentally relevant pesticide formulations affects the expression of stress response genes and its relation to oxidative damage and genotoxicity in Caiman latirostris. Environ. Toxicol. Pharmacol. 2023, 97, 104014. [Google Scholar] [CrossRef]
  41. Wang, R.; Yang, X.; Wang, T.; Kou, R.; Liu, P.; Huang, Y.; Chen, C. Synergistic effects on oxidative stress, apoptosis and necrosis resulting from combined toxicity of three commonly used pesticides on HepG2 cells. Ecotoxicol. Environ. Saf. 2023, 263, 115237. [Google Scholar] [CrossRef]
Chemosensors 12 00073 g001
Figure 1. Integral toxicity of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using the V. aquamarinus VKPM B-11245 strain: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Figure 1. Integral toxicity of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using the V. aquamarinus VKPM B-11245 strain: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Chemosensors 12 00073 g001
Chemosensors 12 00073 g002
Figure 2. Genotoxicity of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG1655 (pRecA-lux), E. coli MG1655 (pDinB-lux), E. coli MG1655 (pColD-lux), E. coli MG1655 (pAlkA-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Figure 2. Genotoxicity of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG1655 (pRecA-lux), E. coli MG1655 (pDinB-lux), E. coli MG1655 (pColD-lux), E. coli MG1655 (pAlkA-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Chemosensors 12 00073 g002
Chemosensors 12 00073 g003
Figure 3. Pro-oxidant properties of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG 1655 (pKatG-lux), E. coli MG 1655 (pOxyR-lux), E. coli MG 1655 (pSoxS-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Figure 3. Pro-oxidant properties of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG 1655 (pKatG-lux), E. coli MG 1655 (pOxyR-lux), E. coli MG 1655 (pSoxS-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Chemosensors 12 00073 g003
Chemosensors 12 00073 g004
Figure 4. Protein- and membrane-damaging properties of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG1655 (pGrpE-lux) and E. coli MG1655 (pFabA-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Figure 4. Protein- and membrane-damaging properties of agricultural soils under soya crops (G), sunflower (H); wheat grown after soya (T(g)) and after sunflower T(h), determined using E. coli MG1655 (pGrpE-lux) and E. coli MG1655 (pFabA-lux) strains: c—control, f—fertilizers, p—pesticides, f + p—combined treatment. Before—sampling before pesticide application. After—sampling after pesticide application at the end of the growing season.
Chemosensors 12 00073 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Khmelevtsova, L.; Klimova, M.; Karchava, S.; Azhogina, T.; Polienko, E.; Litsevich, A.; Chernyshenko, E.; Khammami, M.; Sazykin, I.; Sazykina, M. Biosensor-Based Assessment of Pesticides and Mineral Fertilizers’ Influence on Ecotoxicological Parameters of Soils under Soya, Sunflower and Wheat. Chemosensors 2024, 12, 73. https://doi.org/10.3390/chemosensors12050073

AMA Style

Khmelevtsova L, Klimova M, Karchava S, Azhogina T, Polienko E, Litsevich A, Chernyshenko E, Khammami M, Sazykin I, Sazykina M. Biosensor-Based Assessment of Pesticides and Mineral Fertilizers’ Influence on Ecotoxicological Parameters of Soils under Soya, Sunflower and Wheat. Chemosensors. 2024; 12(5):73. https://doi.org/10.3390/chemosensors12050073

Chicago/Turabian Style

Khmelevtsova, Ludmila, Maria Klimova, Shorena Karchava, Tatiana Azhogina, Elena Polienko, Alla Litsevich, Elena Chernyshenko, Margarita Khammami, Ivan Sazykin, and Marina Sazykina. 2024. "Biosensor-Based Assessment of Pesticides and Mineral Fertilizers’ Influence on Ecotoxicological Parameters of Soils under Soya, Sunflower and Wheat" Chemosensors 12, no. 5: 73. https://doi.org/10.3390/chemosensors12050073

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop