Next Article in Journal
The Influence of Adiposity Levels on the Relation between Perfluoroalkyl Substances and High Depressive Symptom Scores in Czech Adults
Previous Article in Journal
Depicting the Profile of METTL3-Mediated lncRNA m6A Modification Variants and Identified SNHG7 as a Prognostic Indicator of MNNG-Induced Gastric Cancer
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Effect of Arsenic on Fluoride Tolerance in Microbacterium paraoxydans Strain IR-1

Applied Microbiology Laboratory, Centre for Rural Development and Technology, Indian Institute of Technology, Delhi 110016, India
Department of Life Sciences, IIS University, Mansarovar, Jaipur 302020, India
Centre for Advanced Studies, Department of Zoology, University of Rajasthan, Jaipur 302004, India
Department of Zoology, S.P.C., Government College, Ajmer 305001, India
Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, C Scheme, Jaipur 302001, India
National Institute of Pharmaceutical Education and Research-Raebareli, Lucknow 226002, India
Authors to whom correspondence should be addressed.
Toxics 2023, 11(11), 945;
Submission received: 26 August 2023 / Revised: 30 October 2023 / Accepted: 9 November 2023 / Published: 20 November 2023
(This article belongs to the Section Toxicity Reduction and Environmental Remediation)


Fluoride (F) and arsenic (As) are two major contaminants of water and soil systems around the globe, causing potential toxicity to humans, plants, animals, and microbes. These contaminated soil systems can be restored by microorganisms that can tolerate toxic stress and provide rapid mineralization of soil, organic matter, and contaminants, using various tolerance mechanisms. Thus, the present study was undertaken with the arsenic hyper-tolerant bacterium Microbacterium paraoxydans strain IR-1 to determine its tolerance and toxicity to increasing doses of fluoride, either individually or in combination with arsenic, in terms of growth inhibition using a toxicity unit model. The minimum inhibitory concentration (MIC)and half maximal inhibitory concentration (IC50) values for fluoride increased, from 9 g/L to 11 g/L and from 5.91 ± 0.1 g/L to 6.32 ± 0.028 g/L, respectively, in the combination (F + As) group. The statistical comparison of observed and expected additive toxicities, with respect to toxicity unit (TU difference), using Student’s t-test, was found to be highly significant (p < 0.001). This suggests the antagonistic effect of arsenic on fluoride toxicity to the strain IR-1. The unique stress tolerance of IR-1 ensures its survival as well as preponderance in fluoride and arsenic co-contaminated sites, thus paving the way for its possible application in the natural or artificial remediation of toxicant-exposed degraded soil systems.

1. Introduction

Arsenic (As) and fluoride (F) are two major environmental toxicants that pose their toxicity to all exposed living organisms. Their worldwide co-occurrence in groundwater, surface water, soil, and sediments is contributed to by geogenic and anthropogenic sources [1,2]. The geogenic sources include natural weathering and dissolution from rocks and volcanic activities [1,3,4]. Anthropogenic activities, like mining, coal combustion, and the industrial production of fluorinated and arsenic-containing compounds, add to the natural levels of these toxicants [5,6].
The co-occurrence and toxicity threats of As and F have been realized in many regions of the world, such as China [7], India [8], Mexico [9], Latin America [10], Pakistan [11], Mongolia [12], and Korea [13].
In nature, fluoride exists as organic or inorganic fluorine compounds, with high reactivity and electronegativity [14]. After the intake of fluoride through food or water, it rapidly reaches the blood through gastrointestinal absorption [15]. Although most fluoride is excreted through urine, chronic exposure to it results in gradual retention in bones and teeth, causing skeletal and dental fluorosis [16]. It also induces nephrotoxicity, neurotoxicity, and neuro-physiological disturbances [17,18,19].
Arsenic is another co-existing natural toxic metalloid, a well-known poison ubiquitous in the environment. The complex reactivity of arsenic is evident with the presence of various oxidation states (for example, −5, −3, 0, and +3) in organic and inorganic compounds. The trivalent (+3) and inorganic forms are more toxic than the organic arsenic compounds and those in other states of oxidation [20]. Arsenic exposure in humans may cause neurological problems, cardiovascular diseases, hyperkeratosis, hypertension, reproductive toxicity, gangrene, and diabetes mellitus, as well as lung, bladder, and kidney cancer, etc. [20]. Arsenic shows its detrimental effects on protein metabolism by reacting with its sulfhydryl groups, resulting in loss/deviation in protein activity [21]. Co-exposure to these toxicants (As and F) exhibits mild to severe toxicological implications, like impaired neurological development and memory loss [22], nephrotoxicity, and the disordering of serum metabolites and the gut biome [23].
The impact of As and F on microbial communities is also significant, as these are the major role players in ecological systems for shaping microbial communities [24,25]. The contaminant loads cause the microbial community to shift from sensitive to tolerant forms, which have developed specific adaptive mechanisms. The tolerance to As and F in microbes can be achieved by removing, reducing, or restricting the toxicant’s entry into the cell or by chemical modification to reduce its toxicity [26].
Various researchers have studied the chromosomal- or plasmid-borne genes responsible for such tolerance mechanisms. The F-resistant crcB gene has been identified and studied in the fluoride-resistant bacteria Pseudomonas aeruginosa, and Enterobacter sp., which have been reported to function as fluoride transporters [27,28]. Similarly, arsenic resistance has been reported to be regulated by ars genes [29],and the biotransformation of arsenic oxidation forms into one another is regulated by the aio operon, including regulatory genes like aioX (encodes for periplasmic AsIII-binding proteins), aioS (encodes for sensor histidine kinases), and aioR (encodes transcriptional regulators), as well as the functional genes aioB and aioA, which encode for the small and large catalytic subunits of the AsIII oxidase enzyme, respectively. Other genes like the cytC and moeA genes encode for the cofactor cytochrome C and the molybdenum cofactor biosynthesis protein [30].
Numerous molecular mechanisms of toxicant tolerance have developed in microbes, enabling their growth and metabolism in natural or artificial stress conditions. These, in turn, execute their role in mineralization, redox transformation, complexation, and the decomposition of xenobiotics, metals, metalloids, and organic and inorganic wastes [31], thus acting as ecological restoration agents.
The stressed environments generally bear a combination of toxic agents, which exert their toxicities on the exposed organisms. However, the toxicity incurred on the organisms does not purely depend on the environmental conditions, as the genetic composition plays an essential role in providing tolerance or sensitivity [32]. The ecotoxicological studies regarding single or multi-toxicant-contaminated environments on biota provide important information about the impact and interactive possibilities on the exposed populations. In ecotoxicological bioassays, sensitive or tolerant organisms are used as target organisms to estimate the extent and mechanism of toxicity of a single or multi-toxicant environment. Regarding bacteria as the target organism, the popular ecotoxicological tests include the estimation of toxicity in terms of growth inhibition, reduction in enzymatic processes, bioluminescence, etc. [33,34,35,36,37,38]. Further statistical modeling is employed to understand the kind of interaction between the co-existing toxicants on the test organism and also to provide a predictive relationship between the toxicants [39,40,41].
The two most accepted ecotoxicological concepts are concentration addition (CA) and independent action (IA) [42,43,44]. The CA is relevant if only one toxicity mode applies to all the toxicants in a mixture. However, if different modes of toxicity are employed, IA is applicable. The ease of toxicant load estimation and the simple calculation of the CA model make this model more popular and convincing to the scientific community, in which the toxicity of a mixture is estimated based on individual toxicities of different toxicants [42,45]. Moreover, as the observed toxicities of a mixture are infrequently above the CA expected, it is considered a prudent first tier for environmental risk assessment [46]. In the CA concept, the toxicity units (TUs) of each toxicant are estimated, with the ratio of the toxicant concentration and IC50 value, followed by the summation of individual TUs to estimate the expected TUs of the mixture [47]. The observed toxicity values provide information about the additive and subtractive toxicities for synergistic or antagonistic interactions [34].
Several researchers have reported on the individual toxicity of arsenic and fluoride, in terms of reducing or inhibiting growth and reducing catabolic activity in microorganisms [48,49]. However, a lack of data exists regarding the impacts of combined exposure to fluoride and arsenic on microorganisms. In this respect, the study of tolerance to fluoride in combination with arsenic can provide insight into the natural stress impact of toxicants and the subsequent effect on microbial communities.
The ecotoxicological approach of As and F interactive toxicity estimation on bacteria formed the basis of this study, in which the arsenic tolerant Microbacterium paraoxydans IR-1 was used as the target organism. The strain IR-1 was previously isolated in our laboratory and was reported as an arsenic (As III) hyper-tolerant bacteria [50].The importance of using bacteria of the genus Microbacterium as a target organism is understood by its significant ecological role. The bacteria of the genus Microbacterium form an ecologically important entity, with extreme tolerance to environmental contaminants and great potential for the bioremediation of toxicants [50,51,52,53,54,55,56].
The extreme and diverse contaminant tolerance instigated the investigators of this research to study the Microbacterium paraoxydans strain IR-1’s tolerance to fluoride, a major groundwater contaminant in the region. Subsequently, the impact of the co-exposure of As and F was estimated by experimental and modeling studies. The toxicity unit model [34] was adopted to determine the toxicity incurred per unit of concentration increase in toxicants, in single and combined exposure groups.

2. Materials and Methods

2.1. Bacterial Strain

Microbacterium paraoxydans strain IR-1, which has been studied for its hyper-tolerance to arsenic (As III), was used in this study. The isolation and characterization of this strain were described in our earlier study [50]. The 16S rDNA sequence has been deposited in GenBank with the accession number KP730604. The strain IR-1 was grown and maintained in nutrient broth (peptic digest of animal tissue 5 g/L, sodium chloride 5 g/L, beef extract 1.5 g/L, yeast extract 1.5 g/L, pH 7.4 ± 0.2) at 37 °C for 24–48 h at 120 rpm in a shaker incubator (Genei).

2.2. Estimation of Fluoride Content in Soil

The estimation of the fluoride content of the bacterial isolation source (soil) was performed by following the method given by [57], using an Orion ion analyzer (Orion, Seattle, WA, USA). It consists of a cell with an ion selective electrode and a calomel reference electrode, used to determine the cell potential of standard fluoride solutions. The standard fluoride solutions (MERK MILLIPORE, Burlington, MA, USA; supelco-cat no-119814) of 0.1, 1.0, 10.0, 100, and 1000 ppm were prepared, and the pH was adjusted to 5.35 using a total ionic strength adjusting buffer (TISAB-MERK supelco cat no-89465), followed by the determination of the cell potential of each standard solution. The standard calibration graph was constructed by plotting the cell potential versus log (F), which was used to estimate the unknown fluoride concentration in the soil sample (1 g of soil sample in 50 mL distilled water). The detection limit of the instrument was 0.025–500 ppm.

2.3. Dose–Response Relationship Evaluation

To establish the dose–response relationship of toxicants (As and F) on the growth of strain IR-1, it was grown in nutrient broth for 24–72 h and supplemented with increasing doses of the toxicants in three groups, along with a control, to which no toxicant was added (Table 1). The growth in each group was measured in terms of optical density at 600 nm using a double beam spectrophotometer (Schmatzu UV-1800) in a quartz cuvette with 99.5% accuracy, and each group’s minimum inhibitory concentration (MIC) was determined [50]. The toxicants were weighed accurately using a Sartorius weighing balance (Model no: BSA224S-CW) with a detection limit of 0.1 mg.
Determination of Inhibitory Concentration. The toxicant concentration which resulted in a 50% inhibition in growth, i.e., the inhibitory concentration (IC50) for each group, was calculated. The inhibition in growth with the supplementation of toxicants (As/F) was calculated as a percentage, with respect to the control (100% growth). The average growth inhibition values at increasing doses were subjected to regression analysis, to draw a linear relationship between the toxicant concentration (As/F) and the percentage of inhibition. Further, the IC50values of each group (I, II, and III) were deduced from the regression line. The IC50 values of groups III and II were compared statistically using Student’s t-test [34,58].
Estimation of Toxicity Units (TUs) of Toxicants. In the present study, the toxicity unit model was used with a modification, namely, keeping one of the toxicants (As) constant at the IC20 value (2.5 g/L). In order to compare the toxicity of the two toxicants, it was suitable to express the concentration in terms of the toxicity unit (TU), which was calculated using Equation (1) [34]:
TU = MIC IC 50
To study the interactive effect of the two toxicants (F + As) in Group III, two equations, (2) and (3), were drawn. In these equations, the expected TU (TUexp) and observed TU (TUobs) were calculated as the sum of the toxicity units of the two toxicants, with respect to the concentration of the toxicant. The TUexp is a measure of the predicted toxicity, calculated by the summation of the individual toxicity of each toxicant at the particular concentration (MIC), whereas the TUobs was calculated by the summation of TU of As at the dose taken and the TUF+As (Group III) at a particular dose, which were experimentally observed (Table 1 and Table 2). In Equations (2) and (3), the toxicity unit of arsenic is shown as TUAs, fluoride as TUF, and TUF+As is the toxicity unit of the combination group (MIC of Group III/IC50 Group III):
TU exp = ( TU As × C As ) + ( TU F × C F )
TU obs = ( TU As × C As ) + ( TU F   + As × C F )
Further, the statistical comparison of TUexp and TUobs was performed using Student’s t-test with a null hypothesis proposal of no interaction between the toxicants (fluoride and arsenic). The Student’s t-test comparison involves the calculation of the difference (TUdiff) between the expected response and the observed response (Equation (4)), followed by the calculation of the standard error (SE = standard deviation/√N; where N is the number of replicates) and the estimation of SEdiff (Equation (5)). Student’s t-value was calculated (Equation (6)) and compared at p < 0.05–p < 0.001, with the degree of freedom calculated using Equation (7) [34,58,59]:
TU diff = TU exp TU obs
SE diff = ( SE exp ) 2 + ( SE obs ) 2
t = TU diff SE diff
Degree   of   freedom ( d . f . ) = d . f . ( exp ) + d . f . ( obs )

2.4. Determination of pH

The pH of the medium was also measured in all the groups at the 24 h interval, using a pH meter (Electronic India, Panchkula, India; digital pH meter model-III E) calibrated with a pH 4 and a pH 7 buffer [60,61]. The pH values of Group III were compared statistically with Group II, Group I, and the control group, using Student’s t-test [58].

3. Results

The isolation source (soil) of the bacterium M. paraoxydans strain IR-1 was found to be contaminated with 40.43 mg/kg of fluoride (from the present study) and 84 mg/kg of arsenic, as per our prior study [50].

3.1. Dose–Response Relationship

The dose–response relationships of all the studied groups are represented in Figure 1 and Figure 2.

3.2. Determination of Inhibitory Concentrations

The dose relationship graph (Figure 1) depicts a toxicant dose-dependent decline in IR-1 growth, with a MIC of 9 g/L for both As (Group I) and F (Group II). Interestingly, in the presence of a constant dose of As (2.5 g/L), the MIC for F was estimated at 11 g/L, as seen in Group III. IC50 values for all the three groups were calculated from the respective regression line (Table 2; Figure 2), which also shows a similar pattern, with higher IC50 values in Group III (6.322 ± 0.0279 g/L) compared to Group II (5.91 ± 0.01 g/L).
The present study focuses on the combined exposure of two toxicants, i.e., fluoride and arsenic on the growth of the bacterial strain M. paraoxydans IR-1. In the combination (F + As) group, the MIC increased by 2 g, compared to fluoride group, along with a highly significant (p < 0.001) increase in the IC50 values (Table 2 and Figure 2). These results suggest that the presence of arsenic in the medium reduces the toxic impact of increasing doses of fluoride, enabling the strain’s survival even an increased fluoride dose (Figure 1).

3.3. Estimation of Toxicity Units of Toxicants

The calculated toxicity units (TUs) of all three groups, as shown in Table 2, was used to estimate the expected and observed toxicity unit (Table 3).The final comparison was undertaken by estimation of TUdiff and the t-value calculation, which compares the expected toxicity units (TU exp) and observed toxicity units (TUobs) of Group II (F) and Group III (F + As). The measurement of toxicity units (TU) provides a fairly good idea of the extent of the toxicity incurred upon the biological agent per unit concentration of toxicants.
Here, the estimation of TU difference of expected toxicity units (TUexp) with the observed toxicity units (TUobs). in the case of combined exposure to both toxicants (Group III) was found to be highly significant (p < 0.001). This shows that the presence of arsenic in the growth medium of bacterium IR-1 can reduce the toxicity levels of fluoride at each successive dose. This significant difference in TUexp and TUobs is considered antagonism, with a reduction in the toxicity of fluoride due to the presence of arsenic.
As per the model of toxicity units used in the present study, if there is no interaction between the toxicants, then the toxicity of the mixture would be determined by the toxicant with the greatest number of TUs present. If the two toxicants have a synergistic interaction, then the toxicity in the combination group would be calculated via the summation of the individual TUs, whereas in an antagonistic interaction, the toxicities of the combination group should be lower than the individual TUs. On the basis of the results obtained in the study, as shown in Table 3, the observed toxicity of the combination group (TUobs) was significantly lower than individual expected toxicities (TUexp) to the strain IR-1, thus establishing antagonism. Hence, it can be suggested that the presence of arsenic in media reduces the toxicity of fluoride and enables the M. paraoxydans IR-1 to survive at high doses of fluoride.
The toxicity of fluoride can be attributed to its chemical nature, as it is the most electronegative of all the elements; thus, it has a strong tendency to acquire a negative charge. Fluoride ions have the same charge and nearly the same radius as hydroxide ions and may replace each other in mineral structures [62]. Fluoride, therefore, can form complexes with a number of cations. Fluoride can act on bacterial cells via its inhibitory action on enzymes, such as glycolytic enzymes, enolase, and heme-based peroxidases. However, the most important factor of fluoride inhibition is its weakly acidic character, as it enhances the permeability of the membrane to protons, thus compromising the function of F-ATPases in exporting protons. This induces cytoplasmic acidification and results in the inhibition of glycolytic enzymes, as reported in a study of oral bacteria [62,63].
The impact of the toxicity of fluoride has been reported in propionate- and butyrate-degrading microorganisms as well as in mesophilic, thermophilic and acetate-utilizing methanogens, which are the main microbial population in wastewater responsible for organic constituent removal; these showed IC50 values of fluoride ranging from 18 to 43 mg/L, whereas nitrifying bacteria showed the IC50 value of fluoride as 149 mg/L [64]. Other microbial populations, i.e., glucose fermenters, aerobic glucose-degrading heterotrophs, denitrifying bacteria, and H2 utilizing methanogens, were able to tolerate a high fluoride concentration (>500 mg/L) [64]. Although fluoride appears to be toxic for microbial growth and metabolism, M. paraoxydans IR-1, investigated in this study, is able to resist a comparatively much higher fluoride concentration, and toxic effects appeared only at higher doses, with an IC50 value of 5.91 ± 0.01 g/L and a MIC of 9 g/L.
The possible mechanism of fluoride resistance in bacteria has been explored by many researchers. Continuous fluoride stress was found to induce the production of anion-binding ionophores, which can concentrate fluoride and thus reduce its availability [57]. The development of fluoride resistance can also be attributed to genetic change by mutation in the F0-F1 ATPase gene cluster, which has been studied for single nucleotide polymorphism in the fluoride-tolerant bacteria Streptococcus mutans [65]. Fluoride stress is known to trigger riboswitches, like the cbcB and eriC genes, which play a role in inducing the production of anion transporters and other important metabolic pathways [66]. Fluoride resistance in bacteria is also been explained by the evolution of a family of highly selective “Fluc” F-channels that export this inhibitory anion from its cytoplasm [63]. However, the genetic studies were not performed in the present study, but the bacterium IR-1 might apply any of the above-mentioned fluoride tolerance strategies to combat fluoride-induced toxic effects. Various studies on the interactive effects of fluoride and arsenic in higher organisms were summarized in a review [5], wherein the complexity of co-exposure was discussed. In some studies, the synergistic effect of co-exposure was observed, while others reported an antagonistic interaction [67]. The impact of the antagonism of fluoride and arsenic on renal function in a Chinese population was reported [68]. In a brain efficiency study on zebrafish, combined arsenic and fluoride exposure exhibited antagonism in terms of stress markers [69]. However, some research suggests that the dose and duration of arsenic and fluoride exposure also plays a role in determining the synergistic or antagonistic effects [2]. Exposure to As and/or F in a mammalian system has been reported to cause oxidative stress, DNA damage, and perturbations with protein strength [15,70]. Similarly, endoplasmic reticulum stress (ERS)-induced apoptosis has been reported to be the primary mechanism of As- and F-induced injury in H9c2 cells and a rat heart tissue model. Furthermore, the factorial analysis helped to determine the antagonistic toxicological implications in the co-exposure group, with a significant decrease in the expression of the transcription factor CHOP (C/EBP homologous protein), which is involved in ERS-induced apoptosis [65].
The microbial population also shows variability in behavior under toxicant stress. The extent of toxicity incurred on microbes due to toxicant exposure is far more complex, due to the evolution of various tolerance mechanisms. Thus, the exact mechanism of the antagonistic effect of arsenic on fluoride toxicity for the arsenic-resistant bacterium IR-1 is unclear. The antagonism observed in the present study may be because of the formation of AsF5, which can reduce the effective concentration of fluoride.
The results of pH estimation of the medium, with and without culture (IR-1), for all the doses, brings us to a possible explanation of this antagonism (Table 4): the growth of M. paraoxydans IR-1 and the addition of NaAsO2 raises the pH, which can counterbalance the lowering of the pH due to the addition of NaF. Thus, the fluoride tolerance of IR-1 can be attributed to its tendency to raise the pH of the medium, along with its growth, as the acidic character of sodium fluoride is the main factor responsible for its toxicity [63]. Similarly, the rise in pH values with the addition of arsenic to the medium might provide survival benefits to IR-1. Overall, the toxicity of fluoride and its antagonism with arsenic appears to be complex, involving the characteristics of the bacterium M. paraoxydans IR-1 and the mineral phases in the medium.
Microbacterium are extremophiles with a known tolerance to many metal contaminants, and they have shown potential for use in bioremediation [50,55]. The extreme and varied pollutant tolerance, as well as the diverse habitat survival properties of the genus Microbacterium, gives an insight about its possible role in soil recovery and the bioremediation of pollutants in the native soil system. The survival of bacteria under stress conditions can play a crucial role in the mineralization of organic material and the biogeochemical cycling of minerals. However, the precise mechanism of tolerance and the actual role of the strain IR-1 in soil recovery, considering limiting factors and its interactive effect with the native community, need to be explored in soil microcosms in the future.

4. Conclusions

The exposure of Microbacterium paraoxydans IR-1 to increasing doses of the two toxicants (As and F) undertaken in this study resulted in a gradual decline in growth, with a MIC of 9 g/L for each toxicant. Interestingly, in the combination (F + As) group, the MIC increased by 2 g, compared to the fluoride alone group, along with a highly significant (p < 0.001) increase in the IC50 values. Further, the toxicity unit model provided the statistical basis for understanding the interactive effect of both toxicants on M. paraoxydans strain IR-1. The highly significant (p < 0.001) difference (TUdiff =1.47) between the expected (TUexp = 21.60) and observed toxicities (TUobs = 20.32) helped to infer that the presence of arsenic in the medium exhibits an antagonistic effect on fluoride toxicity to the bacterium. The significance of the study lies in the unique fluoride tolerance property of M. paraoxydans IR-1, which seems to provide support to its comparative growth and preponderance in stressed geological systems. This, in turn, can contribute to the bioremediation and recovery of degraded land systems via the detoxification, removal, and degradation of toxicants.

Author Contributions

Conceptualization, P.B., S.J.S.F. and P.K.; data curation, M.M., N.R., T.S. and P.K.; funding acquisition, P.B., S.J.S.F. and P.K.; methodology, M.M., N.R. and P.K.; supervision, P.K. and M.K.M.; writing—original draft, M.M., M.K.M., N.R., T.S., N.J., M.K.S. and P.K.; writing—review and editing, M.M., M.K.M., R.K., N.J., M.K.S., P.B., S.J.S.F. and P.K. All authors have read and agreed to the published version of the manuscript.


The financial assistance used to conduct this research was provided by the Defense Research and Development Establishment (DRDE), Gwalior, India (No.: DRDE-P1-2009/Task118) and the University Grants Commission, India [UGC- F.30-91/2015-BSR]. The APC funding is requested from the institution.

Data Availability Statement

The research data of the study has been provided in the manuscript in figures and tables. The raw data can be provided by the corresponding author on demand.


We extend our gratitude to the Public Health Engineering Department Laboratory, Jaipur and National Test House, Jaipur, for the fluoride and arsenic estimations.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Maity, J.P.; Vithanage, M.; Kumar, M.; Ghosh, A.; Mohan, D.; Ahmad, A.; Bhattacharya, P. Seven 21st Century Challenges of Arsenic-Fluoride Contamination and Remediation. Groundw. Sustain. Dev. 2021, 12, 100538. [Google Scholar] [CrossRef]
  2. Mondal, P.; Chattopadhyay, A. Environmental Exposure of Arsenic and Fluoride and Their Combined Toxicity: A Recent Update. J. Appl. Toxicol. 2020, 40, 552–566. [Google Scholar] [CrossRef]
  3. Alarcón-Herrera, M.T.; Martin-Alarcon, D.A.; Gutiérrez, M.; Reynoso-Cuevas, L.; Martín-Domínguez, A.; Olmos-Márquez, M.A.; Bundschuh, J. Co-occurrence, possible origin, and health-risk assessment of arsenic and fluoride in drinking water sources in Mexico: Geographical data visualization. Sci. Total Environ. 2020, 698, 134168. [Google Scholar] [CrossRef]
  4. Li, Y.; Bi, Y.; Mi, W.; Xie, S.; Ji, L. Land-use change caused by anthropogenic activities increase fluoride and arsenic pollution in groundwater and human health risk. J. Hazard. Mater. 2021, 406, 124337. [Google Scholar] [CrossRef]
  5. Chouhan, S.; Flora, S. Arsenic and fluoride: Two major ground water pollutants. Indian J. Exp. Biol. 2010, 48, 666–678. [Google Scholar]
  6. Kumar, M.; Goswami, R.; Patel, A.K.; Srivastava, M.; Das, N. Scenario, perspectives and mechanism of arsenic and fluoride Co-occurrence in the groundwater: A review. Chemosphere 2020, 249, 126126. [Google Scholar] [CrossRef]
  7. Wen, D.; Zhang, F.; Zhang, E.; Wang, C.; Han, S.; Zheng, Y. Arsenic, fluoride and iodine in groundwater of China. J. Geochem. Explor. 2013, 135, 1–21. [Google Scholar] [CrossRef]
  8. Jha, P.K.; Tripathi, P. Arsenic and fluoride contamination in groundwater: A review of global scenarios with special reference to India. Groundw. Sustain. Dev. 2021, 13, 100576. [Google Scholar] [CrossRef]
  9. Armienta, M.A.; Segovia, N. Arsenic and fluoride in the groundwater of Mexico. Environ. Geochem. Health 2008, 30, 345–353. [Google Scholar] [CrossRef]
  10. Alarcón-Herrera, M.T.; Bundschuh, J.; Nath, B.; Nicolli, H.B.; Gutierrez, M.; Reyes-Gomez, V.M.; Nuñez, D.; Martín-Dominguez, I.R.; Sracek, O. Co-occurrence of arsenic and fluoride in groundwater of semi-arid regions in Latin America: Genesis, mobility and remediation. J. Hazard. Mater. 2013, 262, 960–969. [Google Scholar] [CrossRef]
  11. Farooqi, A.; Masuda, H.; Firdous, N. Toxic fluoride and arsenic contaminated groundwater in the Lahore and Kasur districts, Punjab, Pakistan and possible contaminant sources. Environ. Pollut. 2007, 145, 839–849. [Google Scholar] [CrossRef]
  12. Guo, H.; Zhang, Y.; Xing, L.; Jia, Y. Spatial variation in arsenic and fluoride concentrations of shallow groundwater from the town of Shahai in the Hetao basin, Inner Mongolia. Appl. Geochem. 2012, 27, 2187–2196. [Google Scholar] [CrossRef]
  13. Kim, S.-H.; Kim, K.; Ko, K.-S.; Kim, Y.; Lee, K.-S. Co-contamination of arsenic and fluoride in the groundwater of unconsolidated aquifers under reducing environments. Chemosphere 2012, 87, 851–856. [Google Scholar] [CrossRef]
  14. Ahmad, S.; Singh, R.; Arfin, T.; Neeti, K. Fluoride contamination, consequences and removal techniques in water: A review. Environ. Sci. Adv. 2022, 1, 620–661. [Google Scholar] [CrossRef]
  15. Barbier, O.; Arreola-Mendoza, L.; Del Razo, L.M. Molecular mechanisms of fluoride toxicity. Chem. Biol. Interact. 2010, 188, 319–333. [Google Scholar] [CrossRef]
  16. Cao, J.; Bai, X.; Zhao, Y.; Liu, J.; Zhou, D.; Fang, S.; Jia, M.; Wu, J. The relationship of fluorosis and brick tea drinking in Chinese Tibetans. Environ. Health Perspect. 1996, 104, 1340–1343. [Google Scholar] [CrossRef]
  17. Grandjean, P. Developmental fluoride neurotoxicity: An updated review. Environ. Health 2019, 18, 110. [Google Scholar] [CrossRef]
  18. Ibarluzea, J.; Gallastegi, M.; Santa-Marina, L.; Jiménez Zabala, A.; Arranz, E.; Molinuevo, A.; Lopez-Espinosa, M.-J.; Ballester, F.; Villanueva, C.M.; Riano, I.; et al. Prenatal exposure to fluoride and neuropsychological development in early childhood: 1-to 4 years old children. Environ. Res. 2022, 207, 112181. [Google Scholar] [CrossRef]
  19. Veneri, F.; Vinceti, M.; Generali, L.; Giannone, M.E.; Mazzoleni, E.; Birnbaum, L.S.; Consolo, U.; Filippini, T. Fluoride exposure and cognitive neurodevelopment: Systematic review and dose-response meta-analysis. Environ. Res. 2023, 221, 115239. [Google Scholar] [CrossRef]
  20. Medda, N.; Patra, R.; Ghosh, T.K.; Maiti, S. Neurotoxic Mechanism of Arsenic: Synergistic Effect of Mitochondrial Instability, Oxidative Stress, and Hormonal-Neurotransmitter Impairment. Biol. Trace Elem. Res. 2020, 198, 8–15. [Google Scholar] [CrossRef]
  21. Rai, A.; Tripathi, P.; Dwivedi, S.; Dubey, S.; Shri, M.; Kumar, S.; Tripathi, P.K.; Dave, R.; Kumar, A.; Singh, R.; et al. Arsenic Tolerances in Rice (Oryza sativa) Have a Predominant Role in Transcriptional Regulation of a Set of Genes Including Sulphur Assimilation Pathway and Antioxidant System. Chemosphere 2011, 82, 986–995. [Google Scholar] [CrossRef] [PubMed]
  22. González-Alfonso, W.L.; Pavel, P.; Karina, H.-M.; Del Razo, L.M.; Sanchez-Peña, L.C.; Zepeda, A.; Gonsebatt, M.E. Chronic exposure to inorganic arsenic and fluoride induces redox imbalance, inhibits the transsulfuration pathway, and alters glutamate receptor expression in the brain, resulting in memory impairment in adult male mouse offspring. Arch. Toxicol. 2023, 97, 2371–2383. [Google Scholar] [CrossRef] [PubMed]
  23. Tian, X.; Yan, X.; Chen, X.; Liu, P.; Sun, Z.; Niu, R. Identifying Serum Metabolites and Gut Bacterial Species Associated with Nephrotoxicity Caused by Arsenic and Fluoride Exposure. Biol. Trace Elem. Res. 2023, 201, 4870–4881. [Google Scholar] [CrossRef] [PubMed]
  24. Zecchin, S.; Crognale, S.; Zaccheo, P.; Fazi, S.; Amalfitano, S.; Casentini, B.; Callegari, M.; Zanchi, R.; Sacchi, G.A.; Rossetti, S.; et al. Adaptation of Microbial Communities to Environmental Arsenic and Selection of Arsenite-Oxidizing Bacteria from Contaminated Groundwaters. Front. Microbiol. 2021, 12, 634025. [Google Scholar] [CrossRef] [PubMed]
  25. Zhang, X.; Gao, X.; Li, C.; Luo, X.; Wang, Y. Fluoride contributes to the shaping of microbial community in high fluoride groundwater in Qiji County, Yuncheng City, China. Sci. Rep. 2019, 9, 14488. [Google Scholar] [CrossRef]
  26. Da’ana, D.; Zouari, N.; Ashfaq, M.; Abu-Dieyeh, M.; Khraisheh, M.; Hijji, Y.; Al-Ghouti, M. Removal of Toxic Elements and Microbial Contaminants from Groundwater Using Low-Cost Treatment Options. Curr. Pollut. Rep. 2021, 7, 300–324. [Google Scholar] [CrossRef]
  27. Chellaiah, E.R.; Ravi, P.; Uthandakalaipandian, R. High fluoride resistance and virulence profile of environmental Pseudomonas isolated from water sources. Folia Microbiol. 2021, 66, 569–578. [Google Scholar] [CrossRef]
  28. Chellaiah, E.R.; Ravi, P.; Uthandakalaipandian, R. Isolation and identification of high fluoride resistant bacteria from water samples of Dindigul district, Tamil Nadu, South India. Curr. Res. Microb. Sci. 2021, 2, 100038. [Google Scholar] [CrossRef]
  29. Tang, X.; Yu, P.; Tang, L.; Zhou, M.; Fan, C.; Lu, Y.; Mathieu, J.; Xiong, W.; Alvarez, P. Bacteriophages from Arsenic-Resistant Bacteria-Transduced Resistance Genes, which Changed Arsenic Speciation and Increased Soil Toxicity. Environ. Sci. Technol. Lett. 2019, 6, 675–680. [Google Scholar] [CrossRef]
  30. Corsini, P.M.; Walker, K.T.; Santini, J.M. Expression of the arsenite oxidation regulatory operon in Rhizobium sp. str. NT-26 is under the control of two promoters that respond to different environmental cues. Microbiologyopen 2017, 7, e00567. [Google Scholar] [CrossRef]
  31. Mazumder, P.; Sharma, S.K.; Taki, K.; Kalamdhad, A.S.; Kumar, M. Microbes involved in arsenic mobilization and respiration: A review on isolation, identification, isolates and implications. Environ. Geochem. Health 2020, 42, 3443–3469. [Google Scholar] [CrossRef] [PubMed]
  32. Suciu, I.; Pamies, D.; Peruzzo, R.; Wirtz, P.H.; Smirnova, L.; Pallocca, G.; Hauck, C.; Cronin, M.T.D.; Hengstler, J.G.; Brunner, T.; et al. G × E Interactions as a Basis for Toxicological Uncertainty. Arch. Toxicol. 2023, 97, 2035–2049. [Google Scholar] [CrossRef] [PubMed]
  33. Bitton, G.; Koopman, B. Bacterial and enzymatic bioassays for toxicity testing in the environment. Rev. Environ. Contam. Toxicol. 1992, 125, 1–22. [Google Scholar] [CrossRef] [PubMed]
  34. Fulladosa, E.; Murat, J.C.; Martínez, M.; Villaescusa, I. Effect of pH on arsenate and arsenite toxicity to luminescent bacteria (Vibrio fischeri). Arch. Environ. Contam. Toxicol. 2004, 46, 176–182. [Google Scholar] [CrossRef]
  35. Kong, I.C.; Bitton, G.; Koopman, B.; Jung, K.H. Heavy metal toxicity testing in environmental samples. Rev. Environ. Contam. Toxicol. 1995, 142, 119–147. [Google Scholar] [CrossRef] [PubMed]
  36. Strotmann, U.J.; Eglsäer, H.; Pagga, U. Development and evaluation of a growth inhibition test with sewage bacteria for assessing bacterial toxicity of chemical compounds. Chemosphere 1994, 28, 755–766. [Google Scholar] [CrossRef]
  37. Strotmann, U.J.; Pagga, U. A growth inhibition test with sewage bacteria—Results of an international ring test 1995. Chemosphere 1996, 32, 921–933. [Google Scholar] [CrossRef]
  38. Zhou, X.; Sang, W.; Liu, S.; Zhang, Y.; Ge, H. Modeling and prediction for the acute toxicity of pesticide mixtures to the freshwater luminescent bacterium Vibrio qinghaiensis sp.-Q67. J. Environ. Sci. 2010, 22, 433–440. [Google Scholar] [CrossRef]
  39. Bailer, A.J.; Oris, J.T. Estimating inhibition concentrations for different response scales using generalized linear models. Environ. Toxicol. Chem. 1997, 16, 1554–1559. [Google Scholar] [CrossRef]
  40. Beckon, W.N.; Parkins, C.; Maximovich, A.; Beckon, A.V. A General Approach to Modeling Biphasic Relationships. Environ. Sci. Technol. 2008, 42, 1308–1314. [Google Scholar] [CrossRef]
  41. Van der Vliet, L.; Ritz, C. Statistics for analyzing ecotoxicity test data. In Encyclopedia of Aquatic Ecotoxicology; Férard, J.-F., Blaise, C., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 1081–1096. ISBN 978-94-007-5704-2. [Google Scholar]
  42. Barata, C.; Baird, D.J.; Nogueira, A.J.A.; Soares, A.M.V.M.; Riva, M.C. Toxicity of binary mixtures of metals and pyrethroid insecticides to Daphnia magna Straus. Implications for multi-substance risks assessment. Aquat. Toxicol. 2006, 78, 1–14. [Google Scholar] [CrossRef]
  43. Bliss, C.I. The Toxicity of Poisons Applied Jointly. Ann. Appl. Biol. 2008, 26, 585–615. [Google Scholar] [CrossRef]
  44. Loewe, S.; Muischnek, H. Über Kombinationswirkungen. Naunyn-Schmiedebergs Arch. Exp. Pathol. Pharmakol. 1926, 114, 313–326. [Google Scholar] [CrossRef]
  45. Altenburger, R.; Backhaus, T.; Boedeker, W.; Faust, M.; Scholze, M.; Grimme, L.H. Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: Mixtures composed of similarly acting chemicals. Environ. Toxicol. Chem. 2000, 19, 2341–2347. [Google Scholar] [CrossRef]
  46. Backhaus, T.; Faust, M. Predictive Environmental Risk Assessment of Chemical Mixtures: A Conceptual Framework. Environ. Sci. Technol. 2012, 46, 2564–2573. [Google Scholar] [CrossRef] [PubMed]
  47. Ginebreda, A.; Kuzmanovic, M.; Guasch, H.; de Alda, M.L.; López-Doval, J.C.; Muñoz, I.; Ricart, M.; Romaní, A.M.; Sabater, S.; Barceló, D. Assessment of multi-chemical pollution in aquatic ecosystems using toxic units: Compound prioritization, mixture characterization and relationships with biological descriptors. Sci. Total Environ. 2014, 468–469, 715–723. [Google Scholar] [CrossRef] [PubMed]
  48. Chen, S.; Shao, Z. Isolation and diversity analysis of arsenite-resistant bacteria in communities enriched from deep-sea sediments of the Southwest Indian Ocean Ridge. Extremophiles 2009, 13, 39–48. [Google Scholar] [CrossRef]
  49. Mukherjee, S.; Yadav, V.; Mondal, M.; Banerjee, S.; Halder, G. Characterization of a fluoride-resistant bacterium Acinetobacter sp. RH5 towards assessment of its water defluoridation capability. Appl. Water Sci. 2017, 7, 1923–1930. [Google Scholar] [CrossRef]
  50. Kaushik, P.; Rawat, N.; Mathur, M.; Raghuvanshi, P.; Bhatnagar, P.; Swarnkar, H.; Flora, S. Arsenic Hyper-tolerance in Four Microbacterium Species Isolated from Soil Contaminated with Textile Effluent. Toxicol. Int. 2012, 19, 188–194. [Google Scholar] [CrossRef]
  51. Aniszewski, E.; Peixoto, R.S.; Mota, F.F.; Leite, S.G.F.; Rosado, A.S. Bioemulsifier production by Microbacterium sp. strains isolated from mangrove and their application to remove cadmiun and zinc from hazardous industrial residue. Braz. J. Microbiol. 2010, 41, 235–245. [Google Scholar] [CrossRef] [PubMed]
  52. Heidari, P.; Sanaeizade, S. Optimization and Characterization of Lead Bioremediation by Strains of Microbacterium oxydans. Soil Sediment Contam. Int. J. 2020, 29, 901–913. [Google Scholar] [CrossRef]
  53. Henson, M.W.; Santo Domingo, J.W.; Kourtev, P.S.; Jensen, R.V.; Dunn, J.A.; Learman, D.R. Metabolic and genomic analysis elucidates strain-level variation in Microbacterium spp. isolated from chromate contaminated sediment. PeerJ 2015, 3, e1395. [Google Scholar] [CrossRef]
  54. Learman, D.R.; Ahmad, Z.; Brookshier, A.; Henson, M.W.; Hewitt, V.; Lis, A.; Morrison, C.; Robinson, A.; Todaro, E.; Wologo, E.; et al. Comparative genomics of 16 Microbacterium spp. that tolerate multiple heavy metals and antibiotics. PeerJ 2019, 6, e6258. [Google Scholar] [CrossRef] [PubMed]
  55. Lu, P.; Liu, H.; Liu, A. Biodegradation of dicofol by Microbacterium sp. D-2 isolated from pesticide-contaminated agricultural soil. Appl. Biol. Chem. 2019, 62, 72. [Google Scholar] [CrossRef]
  56. Nowicka, D.; Ginter-Kramarczyk, D.; Holderna-Odachowska, A.; Budnik, I.; Kaczorek, E.; Lukaszewski, Z. Biodegradation of oxyethylated fatty alcohols by bacteria Microbacterium strain E19. Ecotoxicol. Environ. Saf. 2013, 91, 32–38. [Google Scholar] [CrossRef]
  57. Chouhan, S.; Tuteja, U.; Flora, S.J.S. Isolation, identification and characterization of fluoride resistant bacteria: Possible role in bioremediation. Prikl. Biokhim. Mikrobiol. 2012, 48, 51–58. [Google Scholar] [CrossRef] [PubMed]
  58. Mishra, P.; Singh, U.; Pandey, C.M.; Mishra, P.; Pandey, G. Application of Student’s t-test, Analysis of Variance, and Covariance. Ann. Card. Anaesth. 2019, 22, 407–411. [Google Scholar] [CrossRef] [PubMed]
  59. Ince, N.H.; Dirilgen, N.; Apikyan, I.G.; Tezcanli, G.; Ustun, B. Assessment of toxic interactions of heavy metals in binary mixtures: A statistical approach. Arch. Environ. Contam. Toxicol. 1999, 36, 365–372. [Google Scholar] [CrossRef]
  60. Buck, R.P.; Rondinini, S.; Covington, A.K.; Baucke, F.G.K.; Brett, C.M.A.; Camões, M.F.; Milton, M.J.T.; Mussini, T.; Naumann, R.; Pratt, K.W.; et al. IUPAC Recommendations 2002. Pure Appl. Chem. 2002, 74, 2169–2200. [Google Scholar] [CrossRef]
  61. Naumann, R.; Alexander-Weber, C.; Eberhardt, R.; Giera, J.; Spitzer, P. Traceability of pH measurements by glass electrode cells: Performance characteristic of pH electrodes by multi-point calibration. Anal. Bioanal. Chem. 2002, 374, 778–786. [Google Scholar] [CrossRef]
  62. Marquis, R.E.; Clock, S.A.; Mota-Meira, M. Fluoride and organic weak acids as modulators of microbial physiology. FEMS Microbiol. Rev. 2003, 26, 493–510. [Google Scholar] [CrossRef]
  63. Ji, C.; Stockbridge, R.B.; Miller, C. Bacterial fluoride resistance, Fluc channels, and the weak acid accumulation effect. J. Gen. Physiol. 2014, 144, 257–261. [Google Scholar] [CrossRef]
  64. Ochoa-Herrera, V.; Banihani, Q.; León, G.; Khatri, C.; Field, J.A.; Sierra-Alvarez, R. Toxicity of fluoride to microorganisms in biological wastewater treatment systems. Water Res. 2009, 43, 3177–3186. [Google Scholar] [CrossRef]
  65. Li, C.; Qi, C.; Yang, S.; Li, Z.; Ren, B.; Li, J.; Zhou, X.; Cai, H.; Xu, X.; Peng, X. F0F1-ATPase Contributes to the Fluoride Tolerance and Cariogenicity of Streptococcus mutans. Front. Microbiol. 2022, 12, 777504. [Google Scholar] [CrossRef]
  66. Baker, J.L.; Sudarsan, N.; Weinberg, Z.; Roth, A.; Stockbridge, R.B.; Breaker, R.R. Widespread Genetic Switches and Toxicity Resistance Proteins for Fluoride. Science 2012, 335, 233–235. [Google Scholar] [CrossRef]
  67. Mittal, M.; Flora, S.J.S. Vitamin E supplementation protects oxidative stress during arsenic and fluoride antagonism in male mice. Drug Chem. Toxicol. 2007, 30, 263–281. [Google Scholar] [CrossRef] [PubMed]
  68. Zeng, Q.; Xu, Y.; Yu, X.; Yang, J.; Hong, F.; Zhang, A. The combined effects of fluorine and arsenic on renal function in a Chinese population. Toxicol. Res. 2014, 3, 359–366. [Google Scholar] [CrossRef]
  69. Mondal, P.; Shaw, P.; Dey Bhowmik, A.; Bandyopadhyay, A.; Sudarshan, M.; Chakraborty, A.; Chattopadhyay, A. Combined effect of arsenic and fluoride at environmentally relevant concentrations in zebrafish (Danio rerio) brain: Alterations in stress marker and apoptotic gene expression. Chemosphere 2021, 269, 128678. [Google Scholar] [CrossRef] [PubMed]
  70. Flora, S.J.S.; Mittal, M.; Pachauri, V.; Dwivedi, N. A possible mechanism for combined arsenic and fluoride induced cellular and DNA damage in mice. Metallomics 2012, 4, 78–90. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Comparative analysis of fluoride and arsenic toxicity on the growth of M. paraoxydans strain IR-1 (in 48 h). The figure depicts the maximum growth in the control group (without any toxicant—100% growth), and a gradual decrease in the growth of the strain when exposed to arsenic (Group I) and fluoride (Group II) individually. But there was a highly significant (p < 0.001) increase in the growth of the strain in the combination group (Group III—As + F,) as per the statistical comparison using Student’s t-test with Group I and Group II. All the experiments were performed in triplicate and error bars denote standard error.
Figure 1. Comparative analysis of fluoride and arsenic toxicity on the growth of M. paraoxydans strain IR-1 (in 48 h). The figure depicts the maximum growth in the control group (without any toxicant—100% growth), and a gradual decrease in the growth of the strain when exposed to arsenic (Group I) and fluoride (Group II) individually. But there was a highly significant (p < 0.001) increase in the growth of the strain in the combination group (Group III—As + F,) as per the statistical comparison using Student’s t-test with Group I and Group II. All the experiments were performed in triplicate and error bars denote standard error.
Toxics 11 00945 g001
Figure 2. Regression lines representing the growth inhibition of M. paraoxydans IR-1 in presence of arsenic (Group I, ■), fluoride (Group II, ▲), and the combination (Group III, ●). The inhibitory concentration of the combination group was higher than the individual exposure to arsenic and fluoride. IC50 values for all the three groups were calculated from the respective regression line.
Figure 2. Regression lines representing the growth inhibition of M. paraoxydans IR-1 in presence of arsenic (Group I, ■), fluoride (Group II, ▲), and the combination (Group III, ●). The inhibitory concentration of the combination group was higher than the individual exposure to arsenic and fluoride. IC50 values for all the three groups were calculated from the respective regression line.
Toxics 11 00945 g002
Table 1. Groups used in this study, with varying doses of toxicants (As and F) in nutrient broth inoculated with a pure culture of M. paraoxydans strain IR-1.
Table 1. Groups used in this study, with varying doses of toxicants (As and F) in nutrient broth inoculated with a pure culture of M. paraoxydans strain IR-1.
GroupsToxicantsDoses of Toxicants Added in Nutrient Broth
ControlNo toxicantBacterium grown without any toxicant
Group ISodium arsenite (NaAsO2) (Himedia)0–9 g/L
Group IISodium fluoride (NaF) (Himedia)0–9 g/L
Group III
Combination group
Combination (F + As):
Sodium fluoride
Sodium arsenite
0–11 g/L
2.5 g/L (Constant)
Table 2. Toxicities of As group, F group, and As + F combined group on M. paraoxydans IR-1.
Table 2. Toxicities of As group, F group, and As + F combined group on M. paraoxydans IR-1.
GroupsToxicantMIC (g/L)IC50 (g/L)CV (%)TU
ControlNo toxicant----
Group IAs (0–9 g/L)94.83 ± 0.0250.881.86 ± 0.01
Group IIF (0–9 g/L)95.91 ± 0.010.331.52 ± 0.003
Group IIIAs (2.5 g/L) +
F (0–11 g/L)
116.32 ± 0.0280.771.42 ± 0.006
As = arsenic; F = fluoride; MIC = minimum inhibitory concentration; CV = coefficient of variance; TU = toxicity unit.
Table 3. Statistical analysis of the F and As + F combined groups’ interaction.
Table 3. Statistical analysis of the F and As + F combined groups’ interaction.
TUexpTUobsTUdiffS.E.difft-ValueTable Value at
df = 6
p < 0.001
21.60 ± 0.1220.32 ± 0.091.470.158.715.96Antagonistic
Table 4. pH values of media supplemented with fluoride individually or in combination with arsenic and subject to inoculation with a culture of M. paraoxydans IR-1.
Table 4. pH values of media supplemented with fluoride individually or in combination with arsenic and subject to inoculation with a culture of M. paraoxydans IR-1.
ToxicantDoses of NaF (g/L) Supplemented in Nutrient Broth
(without IR-1)
NaF7.41 ± 0.20 b7.46 ± 0.247.44 ± 0.13 b7.49 ± 0.27.64 ± 0.15 b7.65 ± 0.147.63 ± 0.127.97 ± 0.217.78 ± 0.05 b7.88 ± 0.087.85 ± 0.12
NaF + As8.39 ± 0.238.38 ± 0.098.37 ± 0.02 b8.53 ± 0.188.67 ± 0.13 b8.70 ± 0.268.88 ± 0.02 a8.85 ± 0.1218.96 ± 0.07 b8.93 ± 0.238.90 ± 0.02 a
(with IR-1)
NaF7.87 ± 0.07 a,b7.79 ± 0.12 a7.85 ± 0.15 a,b7.85 ± 0.28 a7.93 ± 0.08 a,b7.92 ± 0.17 a7.91 ± 0.13 a7.82 ± 0.16 a7.91 ± 0.04 a,b7.87 ± 0.02 a7.92 ± 0.11 a
NaF + As8.45 ± 0.17 a8.70 ± 0.24 a8.48 ± 0.06 a,b8.8 ± 0.09 a9.04 ± 0.12 a,b9.14 ± 0.12 a9.15 ± 0.07 a9.04 ± 0.14 a9.31 ± 0.18 a,b9.27 ± 0.17 a9.17 ± 0.02 a
pH of culture without toxicant: 8.91 ± 0.064, pH of NB: 7.4 ± 0.02, pH of NB with sodium arsenite (As-2.5 g/L): 8.4 ± 0.18. Each value represents mean ± standard error Significance Levels: a = p < 0.001 (highly significant); b = p < 0.05 (statistically significant). Statistical Comparison: 1. NB vs. NB + As—highly significant; 2. NB vs. culture without toxicant—highly Significant; 3. Culture with NaF vs. culture with NaF + As—highly significant; 4. NaF vs. NaF with culture—non-significant; 5. NaF + Ars vs. NaF + As with culture—non-significant.
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

Mathur, M.; Rawat, N.; Saxena, T.; Khandelwal, R.; Jain, N.; Sharma, M.K.; Mohan, M.K.; Bhatnagar, P.; Flora, S.J.S.; Kaushik, P. Effect of Arsenic on Fluoride Tolerance in Microbacterium paraoxydans Strain IR-1. Toxics 2023, 11, 945.

AMA Style

Mathur M, Rawat N, Saxena T, Khandelwal R, Jain N, Sharma MK, Mohan MK, Bhatnagar P, Flora SJS, Kaushik P. Effect of Arsenic on Fluoride Tolerance in Microbacterium paraoxydans Strain IR-1. Toxics. 2023; 11(11):945.

Chicago/Turabian Style

Mathur, Megha, Neha Rawat, Tanushree Saxena, Renu Khandelwal, Neha Jain, Mukesh K. Sharma, Medicherla K. Mohan, Pradeep Bhatnagar, Swaran J. S. Flora, and Pallavi Kaushik. 2023. "Effect of Arsenic on Fluoride Tolerance in Microbacterium paraoxydans Strain IR-1" Toxics 11, no. 11: 945.

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