Research Article | Open Access
Jayeshkumar R. Ruparelia1,3 , Rishit A. Soni2 and Hiren K. Patel3
1Shree P.M. Patel Institute of PG Studies & Research in Science, Anand, Gujarat, India.
2Shree P.M. Patel Institute of Biosciences, Anand, Gujarat, India.
3School of Sciences, P P Savani University, Surat, Gujarat, India.
Article Number: 9290 | © The Author(s). 2024
J Pure Appl Microbiol. 2024;18(2):1151-1166. https://doi.org/10.22207/JPAM.18.2.34
Received: 30 January 2024 | Accepted: 15 April 2024 | Published online: 27 May 2024
Issue online: June 2024
Abstract

Heavy metal contamination is one of the key environmental complications. Due to some disadvantages of conventional methods, the use of active organisms is becoming more popular technique to remove it. In the present study, primarily 35 bacterial strains were discovered in metal containing media. After being identified resistance power to different copper concentrations (100–1000 mg/l), JRHM33 had the highest level of resistance up to 1000 mg/l of copper. Using the 16S rRNA sequencing, bacterial strain JRHM33 was discovered and revealed 99% similarity to pseudomonas aeruginosa. Sequencing and bioinformatics study using conserved domain analysis supported the laccase gene is present in JRHM33 and has classification as a member of the multicopper oxidase superfamily, which has reduction capacity of metal ions. Analysis of phenotype microarray (PM) technology provides an insight into the metabolic profiling of microbial cell into Pseudomonas aeruginosa JRHM33. Furthermore, Using the central composite design of response surface methodology (CCD-RSM), the successive optimization of the process parameters was attempted for the maximum reduction of the copper. Maximum 68.71% Cu reduction was achieved at 6.71 pH, 90.61 min of incubation time, 5 ml of inoculum size, and 113 rpm of agitation. The generated model has R2 value of 0.9834, indicating that the ANOVA gave it a very significant result. The findings of the validation experiment showed a remarkable similarity between the projected and experimental results. It is determined that bacterial strains isolated from metal-contaminated effluent employ their natural capacity to change toxic heavy metals into less harmful or nontoxic forms.

Keywords

Copper (Cu), Pseudomonas aeruginosa, Optimization, Heavy Metal Tolerance, PBD, CCD-RSM, Laccase

Introduction

Numerous kinds of wastewater have been produced as a result of the population boom, urbanization, and industrialization.1 Because wastewater contains a wide range of organic and inorganic contaminants, it can contaminate soil, groundwater, and surfaces.2 One of the foremost difficulties globally is the pollution of heavy metals.3,4 Metal ion concentrations in some industrial wastes are typically far higher than allowable limits. One such business that produces a lot of waste water with a high concentration of metals is electroplating. Cd, lead, mercury, Cr, Ni, Cu, Zn, Co, and so on are some of these metals. Because heavy metals are harmful even at extremely low quantities, they can harm the environment and subsequently cause a variety of disorders in humans.5 Food, air, and water can all allow them to enter the human body, where they can eventually bioaccumulate.6 Heavy metals in wastewater tend to accumulate because they are not soluble in it and cannot be broken down. Since many companies produce a great deal of hazardous pollution issues for the environment, removing dangerous heavy metals from wastewater is regarded as among the most essential topics of water treatment.7

Dissimilar organic pollutants, there is no complete chemical or biological breakdown of heavy metals. They can therefore only be changed into less dangerous or absorbed forms. One novel technique for treating contaminated wastewater is bioremediation. In bioremediation procedures, tolerance to contaminants, particularly heavy metals, and microbial resistance are crucial. Numerous studies have revealed that certain bacteria are capable of withstanding heavy metals by either removing them from the environment or converting them into forms that are entirely safe or less hazardous, which they then use in their metabolic processes to continue growing.8 The study’s objective was to eliminate copper ions from a water-based mixture using microorganisms. We conducted a central composite design examination to identify important factors that influence the elimination of heavy metal from aqueous solution by bacterial isolates and comprehend their impact on the process.9 We also isolated and identified heavy metal tolerant bacteria, determined the tolerance power of heavy metal tolerating microbes, and selected highly potential bacterial strains for bioremediation of heavy metal. A CCD, which provides a scientific model that illustrates the impact of every variable, was utilized to investigate the impacts of several variables on heavy metal removal, including pH, incubation period, inoculum size, agitation, and primary content of Cu+2.10

The variables participate in a testing are changing concurrently in the statistical design. The two most significant benefits are that the interaction of two or more variables may also be determined, in addition to evaluating the impacts of distinct parameters and how significant they are in a certain process.11 Because of this, it uses low-tech, low-cost treatments that are widely accepted by the public and may frequently be completed on-site.12

Materials and Methods

Sample collection
Water samples and sludge were taken from wastewater discharge locations of three distinct electroplating industries that were contaminated with heavy metals. The samples were taken from the same electroplating industry, Makarpura GIDC, located in Baroda, Gujarat, India. The waste product of this sector is copper (Cu), which is used to metal plate automobile gasoline pipes. Following standard procedures, wastewater and sludge samples were collected and aseptically transferred to the lab in screw-cap sampling vials at 4°C.13

Isolation of bacteria
Using a nutrient agar medium (HiMedia), the heavy metal resistance bacterial strains were isolated by a serial dilution procedure. In 10 milliliter Erlenmeyer flasks with distilled water, one gram of sludge was added, and the mixture was stirred for 20 minutes at 200 rpm. Each suspension was diluted in steps of 10-1 to 10-6. To separate the organisms from diluted samples, the Spread plate technique was used. Using a glass spreader, 0.1 ml was pipetted onto nutrient agar plates, which were then incubated for 24 hours at 37°C. The nutrient broth (100 ml) was directly inoculated with 10 ml of the polluted water from the wastewater sample, and it was then incubated for 24 hours at 150 rpm and 37°C in an incubator shaker (REMI). Utilizing a glass spreader, 0.1 ml of nutrient broth containing the contaminated water sample was applied to the nutrient agar plates and incubated for 24 hours at 37°C in an incubator.14 The strain with the resistance to a copper heavy metal was exposed to further examination.

Primary screening for heavy metal-resistant bacteria
To do a preliminary screening for bacterial resistance to heavy metals, each bacterial isolate was distributed independently onto nutrient agar plates with a medium containing 100 parts per million of CuSo4. After preparing copper-adjusted nutritional agar medium in distilled water, bringing the pH to 7.5, and incubating the plates at 37°C for 24 hours, bacterial colony development was observed.

Secondary screening for selection of most efficient heavy metal resistant bacteria
To find the most effectual heavy metal resistance bacterial, secondary screening is done with two strategies; one is all the bacterial isolates were screened for a potential heavy metal tolerance test and the second is to check bacterial growth in the presence of copper heavy metal

Determination of MIC or Heavy metal tolerance test
In these studies on heavy metal removal, the bacterial strains used must be resistant to a specific concentration of heavy metal. The minimum inhibitory concentration test was used to determine whether an identified strain of bacteria was resistant to the heavy metal copper (Cu). Congeevaram et al.,10 where the varying concentrations of metals ranging from 100 ppm to 1000 ppm containing nutrient agar plates were prepared. Stock solutions of metals (CuSO4) were ready in deionized water, decontaminated by filtration, and added to a Nutrient agar medium. All the bacterial strains of 0.1 ml were streaked on above mentioned different concentrations of metal-containing plates, separately. Then all the plates were placed in an incubator and incubated for 48 hours at 37°C. Microbial development on a nutrient agar medium free of heavy metal contaminants served as a positive control.13

Growth in the existence of Cu metal ion
Bacterial growth in the existence of Cu was tested rendering to Deng and Wang15 with changes. Copper was added to a Nutrient broth medium after it had been inoculated with a culture that had been cultured overnight in the medium at 37°C. The following single ingredient concentrations were employed for each mixture: 0 ppm (control), 25 ppm, 100 ppm, 500 ppm, and 1000 ppm. The cultures were incubated at 37°C for 48 hours, and the growth was quantified as an increase in optical density at 600 nm (OD600) using a spectrophotometer (Shimadzu UV-1800).

Characterization of selected isolate
Morphological and biochemical characterization
The form, elevation, edge, surface, and size of the most productive isolates were used to determine the colony morphology, whereas Gram staining was used for microscopic identification. In order to determine the integrity of isolate JRHM33, different biochemical tests were performed, including an oxidase test, a catalase test, an indole production test, a Voges Proskauer test, a Urease test, and a citrate test.16

Metabolic profiling of selected bacterial isolates
Utilization trends for Biolog carbon substrates inoculations were made in duplicate using Biolog GP2 MicroPlates (Biolog, Inc., Hayward, CA, USA) and incubated at 30-35°C for 24-36 h. After 12, 24, 36, and 48 hours, the optical density in each well at 590 nm that results from tetrazolium violet reduction was measured using a Biolog Microplate Reader and MicroLog software (Release version 4.0).17 When a microplate well produced an O.D. 0.59 greater than 0.4 on at least two reading points, it was considered positive.

Molecular identification and phylogenetic analysis of selected bacterial isolates
Using universal forward and reverse primers (27F – 52 AGAGTTTGATCMTGGCTCAG 32 and 1492R – 52 TACGGYTACCTTGTTACGACTT 32 ) in PCR (Polymerase Chain Reaction) the molecular identification of JRHM33 was accomplished.18 A similarity search was done using the online BLAST tool of NCBI for the identification of isolates. Sequenced data has been submitted to GenBank for accession numbering. Additionally, NCBI was used to construct a neighbor-joining tree and conduct an evolutionary analysis.

Molecular characterization for laccase enzyme
Several possible laccase gene sequences were found on NCBI by using bioinformatics. With the use of In silico rapid PCR software, the gene-specific primer was created.17 It was also used to identify the laccase gene found in the bacterial DNA using PCR. The presence of a conserved domain for laccase enzyme was identified using NCBI conserved domain search.

Important variables were screen out for Cu removal using Placket- Burman design
Plackett and Burman19 design was used to eliminate factors that could influence the maximum removal of heavy metal ions using JRHM33 isolates. Important process variables were chosen in accordance with the literature review, and then, for each assay, the copper removal was calculated in terms of percentage (%). Twelve runs of an experimental design with six particular variables and five dummy variables, each with two levels, were used to select significant variables as depicted in Table 1. Variables with a 95% confidence level or higher were regarded as a relevant factor for the greatest Cu removal by JRHM33, and they were further selected for RSM (CCD) level optimization using Design-Expert (version 13.0.1.0) software.

Table (1):
Placket-Burman experimental design was used to remove Cu utilizing independent two-level variables

Factor
Name
Unit
-1
1
1
pH
4
8
2
Incubation Time
Hours
24
120
3
Initial concentration
PPM
10
100
4
Temperature
25
45
5
Inoculum size
ml
01
05
6
Agitation
RPM
0
150
7
G- Dummy: 1
8
H- Dummy: 2
9
I- Dummy: 3
10
J- Dummy: 4
11
K- Dummy:5

Table (2):
Level of the CCD’s experimental variables, both coded and uncoded

Level Uncoded level Coded level
Inoculum size (Baumler, Ma et al.) pH Incubation Time (Hours) Agitation (RPM)
alpha -2 0 2 0 0
Low -1 1 4 24 1
Mid 0 3 6 72 75
High +1 5 8 120 150
Alpha +2 7 10 168 225

Optimization of particular variables for maximizing Copper (Cu) removal by JRHM33 isolates using central composite design of response surface methodology (CCD-RSM)
Using the CCD-RSM design, shake flask trials were conducted, and four important variables were investigated to optimize Cu removal. Thirty run experiment design was conducted with four factors at five levels (Table 2). Wherein, highest (+2), high (+1), medium (0), low (-1), and lowest (-2) were the coded levels. The middle point is made again six times to consider only errors and the lack of fit of the offered model. The following second-order polynomial equation explains the model’s behavior:

Y= β0+∑i βi Xi + ∑ii βiiXi2 + ∑ij  βij Xi Xj

where Y is the expected response (percentage of Cu removed), The linear, quadratic, and interaction coefficients are bi, bii, and bij, respectively, while the model constant is denoted by b0. The system’s independent coded variables are shown by Xi and Xj.20 There was a regression study using the Design-Expert program. The model with the highest F-value, with a p-value less than 0.05 and an insignificant Lack of Fit test, was chosen. Furthermore, a suggested model was evaluated using different fit data metrics such as F-value, R2-coefficient of determination, SNR-value of precision, and the disparity between adjusted and predicted R2-values.21 The appropriateness of the proposed model was also evaluated using different diagnostics tools. Three-factor interaction study employed using surface graphs in three dimensions and counterplots. Finally, optimization was attempted for the maximum heavy metals elimination by optimizing the level of different variables. In the end, authentication trials were conducted to authorize the model’s optimum solution.22

RESULTS AND DISCUSSION

Isolation and screening bacteria resistance to heavy metal
From industrial sludge, 40 distinct bacterial strains were identified. Using the primary screening procedure, 35 Cu-resistant bacterial strains were eliminated out of 40 isolates.23 These 35 isolates were chosen based on their resistance to 100 ppm of Cu and their capacity to withstand heavy metals, which was assessed by raising the concentration of copper in media with a range of 100 ppm to 1000 ppm.

Additionally, most heavy metal-tolerant bacteria were chosen from the selected 35 isolates using a secondary screening method. In the first phase, heavy metal tolerance was studied by incubating selected isolates in a nutrient agar plate containing copper heavy metal. 23 isolates were tolerated up to 200-300ppm of Cu, 10 isolates were tolerated up to 600-700ppm of Cu while 2 isolates JRHM27 and JRHM33 showed growth with 1000ppm of Cu and JRHM33 also grew on 10,000ppm Cu (Figure 1a).

Figure 1. (A) JRHM33 on nutrient agar plate without heavy metal (Cu) and with 100 ppm, 500 ppm & 1000 ppm of heavy metal (Cu) (B) heavy metal Growth of Pseudomonas aeruginosa JRHM33 on Cu-containing nutrient agar and (C) Gram staining of JRHM33

The most resilient bacteria were chosen in the second stage by culturing the chosen bacteria in nutrient broth that contained copper heavy metal. Bacterial growth in the presence of Cu in the nutrient broth after 48 hours, JRHM33 gives the highest growth compared to other isolates. So based on growth and tolerance capacity, JRHM33 was selected for further study. Numerous studies (Abou-Shanab, Van Berkum et al.24, Alam and Malik25) have documented metal resistance in bacteria that have been isolated from soil pollution and wastewater that contains various heavy metals. Batool et al.26 found that two bacterial isolates were resistant up to 1600 ppm of Cr and Cd.

Morphological and biochemical characterization
Considering the ability to tolerate heavy metals and the current growth in Cu heavy metal, most potential heavy metal removal isolate JRHM33 is used for further study and is found gram-negative, short rod bacteria and give bluish green pigmentation on nutrient agar (Figure 1 a, b and c) and were characterized according to their physical, biochemical, and cultural traits (Table 3).

Table (3):
Morphological and biochemical characterization of Pseudomonas aeruginosa JRHM33 isolates

Morphological characteristics
Test
Result
Colony on Nutrient agar
Smooth, Translucent, low convex, and irregular edge
Gram’s reaction
Gram’s Negative
Motility
Motile
Cell size & shaped
Small, rod
Capsule staining
Non-capsulated
Endospore staining
Non-spore forming
Pigment
Bluish-green on Nutrient agar
Biochemical characterization
Test
Result
Catalase
+
Oxidase
+
Citrate
+
Coagulase
Gelatin hydrolysis
+
Indole
Methyl red (MR)
Voges Proskauer (VP)
Urease
Nitrate reduction
+
TSI
Alkali
Cetrimide
+
Fructose
+
Lactose
Mannitol
+
Sucrose
Xylose

Molecular identification and analysis of potential isolates
Molecular identification was done by 16S rRNA sequence identification by HiMedia laboratory, Bombay. The highly similar sequence of the deduced sequence was aligned using BLAST search. A sequence was closely related to the sequence of previously reported bacterial strains, with an average identity of 98%, based on the 16S rRNA sequence analysis by NCBI, the isolates were identified as Pseudomonas aeruginosa and labeled as Pseudomonas aeruginosa JRHM33. The sequence was submitted to the GenBank database and obtained accession number OR105012. The distance tree result revealed the relatedness between the bacterial isolates (Figure 2).27 Prior research has also used BLAST and NCBI to identify bacteria and fungi by DNA sequencing, allowing for species-level identification of the organisms.28,29,30 Similar identification found by Patel et al.18

Figure 2. Phylogenetic analysis of Pseudomonas aeruginosa JRHM33

Metabolic profiling using Phenotypic Microarray (PM) technology
The PM technology enables us to step-by-step examine the unique metabolic pathways of various isolates. Research on the carbohydrates by all the distinct bacteria has demonstrated that, in addition to basic carbohydrate metabolism, these bacterial isolates have a specific capacity to utilize complex carbohydrates. The metabolic processes of Pseudomonas aeruginosa JRHM33 utilized all of the complex sources like D-salicin, Gentibiose, cellobiose, dextrin, a-D-glucose, D-mannose, D-mannitol, etc. Tohsato et al. findings showed that Glyceraldehyde-3-P played a significant role as a molecular switch of the focal metabolic system.31 Baumler also investigated how different enterobacteria might distinguish between species that had been preserved via the PM technique by using carbon, nitrogen, and sulfur. The data demonstrated a phylogenetic link between the tested organism and enterobacteria but also revealed differences in systems biology and the metabolic networks of several catalysts.32 The PM technology was greatly helpful in understanding the central metabolic lignocellulose degradation network with a unique metabolic pathway in Bacillus sp. NAULH2 in banana pseudostem degradation also.17

Characterization of laccase gene
The best temperature for annealing Lac CF and Lac CR primers was found to be between 60.7°C and 64.7°C, according to research on the primer sequence. An exact band of the laccase gene was extracted and purified from the gel. using a specialized column provided by the Thermo Scientific Genjet kit to sequence it. The final sequences for Laccase Pseudomonas aeruginosa JRHM33 were 844 bp. The same software’s homology search revealed 99% similarity between the Pseudomonas aeruginosa genes for copper oxidase, which is correlated with the laccase gene family.

The determined amino acid sequences of the laccase genes from Pseudomonas aeruginosa were compared with known characterized laccase of the multicopper oxidase family using the CLUSTAL-W multiple alignment tools. The Conserved domain of the cupredoxin superfamilies was found by ORF-BLAST analysis of the three laccase amino acid sequences. In the Pseudomonas aeruginosa JRHM33 isolate, the multicopper polyphenol oxidoreductase laccase domain was found at amino acid positions 59–788. Data study shows that this domain contains a trinuclear copper binding site with the copper oxidase 4 superfamily (Figure 3). Many researchers found that removing heavy metals from contaminated water and soil is bioremediation using laccase enzymes. The heavy metal ions are changed from their soluble and toxic form to their insoluble and non-toxic form during laccase-mediated oxidation.33

Figure 3. Conserved domain of laccase gene in Pseudomonas aeruginosa JRHM33

Screening of important variables using Plackett-Burman method for Cu removal
For the selection of significant variables for Cu removal, the Plackett–Burman design was used to examine six process variables over 12 experimental runs with five dummy variables. Table 4 shows the experimental design as well as the recorded response (Cu removal). According to Table 5 statistical analysis of the Cu removal data, among the chosen process variables Inoculum size, pH, incubation time and agitation have a positive effect, whereas Initial concentration has a negative effect on the Cu removal. The same result found by Kumar et al.34 is that more ions compete for those binding sites as concentration increases, and more binding locations become available for ion complexation at the starting concentration, the percentage clearance is reduced as the initial concentration of metal ions increases. The elements’ relative importance is shown by the Pareto chart (Figure 4), where the orange component (dark color bar) indicates a positive influence on Cu elimination and the blue component (light color bar) indicates a negative effect. Inoculum size, pH, incubation time, and agitation had a much stronger influence on Cu removal than the other parameters, as seen by the half-normal plot of effect (Figure 5). All four variables are outliers that sit beneath or to the right of the straightness; however, because they are typically distributed along a straight path, all other variables have less of an impact on the removal of Cu. The ANOVA table’s model F-value of 94.81 indicated that the model is significant. Due to noise, there is only a 0.01 % coincidence that an F-value this large will happen.35 Inoculum size, pH, incubation time, and agitation are all significant model terms with p-values less than 0.05. (95 percent confidence). According to the model, these four process parameters have a substantial impact on Cu removal using JRHM33 isolates (Table 5).

Table (4):
Coded independent variables for Cu removal by Placket – Burman experimental design

 Run
Factor 1 A: pH
Factor 2 B: Incubation time Hrs
Factor 3 C: Initial conc ppm
Factor 4 D: Temp C
Factor 5 E: Inoculation size ml
Factor 6 F: Agitation rpm
Factor 7 G: G
Factor 8 H: H
Factor 9 J: J
Factor 10 K: K
Factor 11 L: L
Response Cu Removal %
1
-1
-1
-1
1
-1
1
1
-1
1
1
1
38
2
-1
1
1
-1
1
1
1
-1
-1
-1
1
60
3
1
1
-1
1
1
1
-1
-1
-1
1
-1
74
4
1
-1
1
1
-1
1
1
1
-1
-1
-1
45
5
-1
-1
1
-1
1
1
-1
1
1
1
-1
52
6
-1
1
-1
1
1
-1
1
1
1
-1
-1
61
7
1
1
-1
-1
-1
1
-1
1
1
-1
1
60
8
1
1
1
-1
-1
-1
1
-1
1
1
-1
47
9
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
31
10
1
-1
-1
-1
1
-1
1
1
-1
1
1
60
11
1
-1
1
1
1
-1
-1
-1
1
-1
1
59
12
-1
1
1
1
-1
-1
-1
1
-1
1
1
36

Table (5):
Statistical study of the Plackett – Burman experimental strategy

Source
Co-efficient estimate
F- value
P-value
Model
104.58
< 0.0001
significant
A-pH
5.58
109.49
< 0.0001
B-Incubation time
4.42
68.51
0.0002
C-Initial conc
-2.08
15.24
0.0079
E-Inoculation size
9.08
289.78
< 0.0001
F-Agitation
2.92
29.88
0.0016

Figure 4. Pareto chart illustrating the significance order for eleven elements

Figure 5. Half-normal plot displays the important effect of the outliers on the Cu removal

Optimization of selected variables for maximizing Cu removal using CCD-RSM
From the Plackett-Burman design, process factors with a substantial effect on Cu removal were chosen, and the CCD-RSM was utilized to evaluate their combined influence and determine the ideal proportion of each variable. Thirty-run experimentation was conducted using 5 levels (rotatable) of each of the 4 variables with 6 replicates at the centre point (Table 6).

Table (6):
Four coded factors with predicted and experimental responses make up the CCD design layout

  Run Factor-1 A: Inoculum size min Factor-2 B: pH Factor-3 C: Incubation Factor-4 D: Agitation time Experimental Answer Copper reduction Predicted Answer
N N % % %
1 5 8 120 0 42 41.75
2 3 2 72 75 10 11.17
3 5 8 24 150 44 45.08
4 7 6 72 75 47 45.00
5 1 4 120 0 23 20.42
6 3 6 72 75 62 67.50
7 3 6 168 75 41 45.67
8 3 10 72 75 30 31.83
9 5 4 24 0 25 26.75
10 5 4 24 150 30 29.75
11 3 6 72 75 67 67.50
12 5 4 120 0 26 25.92
13 3 6 72 0 31 30.50
14 3 6 72 75 67 67.50
15 1 4 24 150 19 17.75
16 1 8 120 150 44 40.75
17 3 6 72 225 45 48.50
18 1 4 120 150 36 33.42
19 3 6 72 75 71 67.50
20 5 8 120 150 63 61.25
21 5 8 24 0 39 40.08
22 0 6 72 75 12 17.00
23 1 8 120 0 27 25.75
24 3 6 72 75 69 67.50
25 3 6 72 75 69 67.50
26 1 8 24 0 23 22.08
27 5 4 120 150 44 43.42
28 3 6 00 75 30 28.33
29 1 4 24 0 19 19.25
30 1 8 24 150 24 22.58

Based on the model statistics data, a quadratic model was suggested by the software. Wherein, the F-value was 64.47. There is just a 0.01 percent chance of a better F-value due to noise. The determination coefficient shows how fine the model fits the statistics, which is nearer to 1 (R2 = 0.9837), indicating a stronger relationship between actual and expected responses. Similarly, there is less than a 0.2% difference between the predicted R2 (0.9272) and the adjusted R2 (0.9684) which is highly desirable for a good model. Secondly, the proposed model has a 24.61 signal-to-noise ratio, suggesting that it is precise enough for the study. According to the F-value of 0.4641, Compared to the pure error, the lack of fit is negligible (a non-significant lack of fit is desirable for the model). The appropriateness of the suggested quadratic model for enhanced Cu removal is supported by all of these fit statistical results (Table 7). In addition, the appropriateness of the suggested paradigm was tested using Design-Expert software’s diagnostics and influence tools. There was a straight line in the normal probability plot, indicating that the residuals were normally distributed. To check for constant variance, a plot of the ratio of residuals to expected responses was examined, which revealed a random scatter plot, indicating that variance did not expand from lower to higher probable scores. The residual versus run plot revealed no discernible trend. Using a box-cox plot, the required power transformation was found. The graphic shows a lambda value range of 0.56 to 1.54 at the 95 percent confidence level, with the optimum lambda at 1.03 (Figure 6). Given that the recommended model’s lambda value is within the given range and is quite close to the optimum lambda value, there was no suggestion for power transformation. All of these diagnostics data points suggest the model’s suitability.23

Table (7):
ANOVA for quadratic model

Source Sum of Squares df Mean Square F-value p-value
Model 9457.13 14 675.51 64.47 < 0.0001 significant
A-Inoculum size 1176.00 1 1176.00 112.24 < 0.0001
B-pH 640.67 1 640.67 61.15 < 0.0001
C-Incubation time 450.67 1 450.67 43.01 < 0.0001
D-Agitation 486.00 1 486.00 46.38 < 0.0001
AB 110.25 1 110.25 10.52 0.0055
AC 4.00 1 4.00 0.3818 0.5459
AD 20.25 1 20.25 1.93 0.1848
BC 6.25 1 6.25 0.5965 0.4519
BD 4.00 1 4.00 0.3818 0.5459
CD 210.25 1 210.25 20.07 0.0004
2283.86 1 2283.86 217.97 < 0.0001
3627.43 1 3627.43 346.20 < 0.0001
1594.71 1 1594.71 152.20 < 0.0001
1344.00 1 1344.00 128.27 < 0.0001
Residual 157.17 15 10.48
Lack of Fit 109.67 10 10.97 1.15 0.4641 not significant
Pure Error 47.50 5 9.50
Cor Total 9614.30 29
R2=0.9837 Pred
R2 = 0.9272
Adj
R2 = 0.9684
AdeqPrec = 24.61

Figure 6. Box-cox plot

The second-order polynomial equation was used to accomplish it with each component in this model to predict the response for given values of each component. The relative influence of the component may be determined by comparing the factor coefficients:

Cu removal= +67.50+7.00 A-5.17B+4.33 C+4.50 D+ 2.63 AB-0.500 AC+1.13 AD+0.6250 BC+0.500 BD+3.63 CD-9.12 A2-11.50 B2-7.62 C2 -7.00 D2

To investigate how interactions affect the identified control factors, a three-dimensional surface plot and a two-dimensional contour plot were produced utilizing the response plotted against another two numeric components while the other parameters were held constant at their mid-values (Figure 7). The result shows that the curvature effect was obtained and that revealed the interaction effect of selected independent variables for the maximum removal of Cu heavy metal. Maximum Cu removal was noticed at around the midpoint value of inoculum size, pH, incubation time, and agitation. The graph shows that Cu removal increases initially and reaches maximum at mid-point value and then it decreases (Figure 7a & 7b).

Figure 7. The action of Cu removal as a function of two-parameter interaction is displayed using three-dimensional surface plots

According to the Design-Expert software, the optimal solution for maximum Cu elimination (68.97%) is shown in Figure 8. Validation trials were supported in triplicates using shake flask studies as per the optimized solution wherein, 5 ml of 108/ml bacterial cell inoculum size, 6.7 pH, 113 rpm agitation speed, and 90.61 min incubation time. The results of the validation experiment were compared to the predicted response data, revealing that the mean (71 percent) elimination of Cu was within the 95 percent prediction interval of 63.99 percent to 73.95 percent. Abou et al. 24 has achieved Cu removal up to 83.3% from industrial wastewater using B. mojavensis C6, similarly; around 85% Cr removal efficiency was found by Chang et al. Cheng et al36 by bacterial isolates using RSM BBD. Another study by Tarangini et al.37 found 60.5 % Cu removal by pseudomonas aeruginosa.

Figure 8. The software’s recommended optimal solution is shown on the desirability plot

Based on RSM using CCD found that a combination of Inoculum size, pH, agitation, and Incubation time have significant impact on the removal of Cu ions.34,38

One important factor in the elimination of Cu is time. The ability of P. aeruginosa to remove various additional metal ions was time-dependent. Removal gets better with passing time, and metal ions in certain situations reveal that they work best at a particular moment in time. As a result, optimization is necessary because the impact of time varies depending on the metal and the bacterium. For P. aeruginosa JRHM33, maximum Cu removal occurs at 90.61 minutes of incubation time; however, if contact time exceeds 90.61, Cu removal declines. Similar results were observed by Deepali12 where maximum Cu removal (80 %) was achieved at 96 hours incubation time using P. putida in optimum conditions. According to Bandela et al.39 reported higher heavy metal removal at 72 hours of incubation time. Furthermore, it was found that the incubation period has a significant effect on the process of metal removal from industrial effluent.

One of the key elements influencing both the metabolic activity of bacteria and the chemical behaviour of metal ions in solution is pH. According to the current research, Cu elimination was higher at pH 6.71 a noticeable drop in Cu removal as the medium’s pH changes. The bioremediation process may vary depending on pH variations, which may be caused by variations in Pseudomonas ligand protonation on the cell surface. The degree of ligand protonation that includes metal binding can be strongly influenced by variations in the external pH of the medium.40 Similar results were observed by Sarin et al.41 At pH 6.8, it was discovered that Pseudomonas fluorescence had a great effectiveness in removing Cd. The ideal pH for bacterial isolates in heavy metal bioremediation was found to be 7.0 in a study, which was higher than the current results.10 The metal ion’s solubility in the medium was modified by pH throughout the bioremediation process, which is mostly dependent on the functional groups present on the bacterial cells’ surfaces.42 Functional groups including hydroxyl, carboxyl, phosphate, and amino play a significant role in the uptake of heavy metals in microorganisms, and their behaviour varies depending on the pH level.43 At pH 7, Ahokkumar et al.44 found that Sphaerotilus natansin removed 48%, 75%, and 52% of Cu, Pb, and Cr, respectively.

Bioremoval of Cr (Ahmaruzzaman and Gupta45) by P. aeruginosa was greatly influenced by inoculum size. The wastewater’s Cu content decreased by 68.97% in just ninety hours, despite the inoculum’s 5 milliliters (108 cells per milliliter). Additionally, as the inoculum size was reduced, the time for Cu reduction increase, and at the optimal inoculum size, the time decreased. According to reports from Mackey and Kerridge46 and Robinson et al.,47 the inoculum size had a minor impact on the eradication of Cr.

Another important factor for Cu removal is the agitation and fluid medium oxygen concentration could be greatly affected by it,48 where the growth of microorganisms can be influenced by the dissolved oxygen. Figure 7 illustrates the impact of agitation on P. aeruginosa’s bio-reduction of Cu. Agitation at 113 rpm resulted in a 68.97% reduction in Cu content. A decrease in Cu was discovered in static conditions, however, it was not as noticeable as under shaking conditions. These findings suggested that P. aeruginosa might be used in both shaking and static circumstances to decrease Cu in the wastewater. However, the rate of Cu reduction under static conditions was lower than that under shaking conditions. As agitation speed increases, copper is removed more efficiently. However, the removal standing of Cu was best after reaching an optimum speed at 113 rpm. Due to Cu high solubility in water, copper residues or soil contaminated by Cu can easily be dissolved into liquid. Therefore, P. aeruginosa has a large deal of potential to remove Cu pollution.10,49 Tarangini & Satpathy50 found that agitation time is a crucial factor in establishing Cr biosorption by the resistance organisms of Bacillus subtilis and Pseudomonas aeruginosa.

CONCLUSION

Pseudomonas aeruginosa JRHM33, the most promising bacterial isolate found thus far, has been shown to grow efficiently, tolerating up to 10,000 parts per million of copper, and to successfully remove copper from an aqueous solution. Pseudomonas aeruginosa was identified using conventional microbiological techniques such as 16S RNA sequencing, and its metabolic system was validated by Biolog analysis. Additionally, a bioinformatics study revealed that the bacterial laccase gene has a multicopper oxidase superfamily conserved domain, providing additional proof that the gene has a laccase conserved domain, which helps to transform more toxic to less toxic form of copper heavy metal. The PM metabolic investigation demonstrated that bacterial isolates had a significant capacity to consume most of the complex carbohydrates. The optimization of various factors affecting bacterial growth using a statistical technique, PBD, and CCD model seems to be a helpful resource for forecasting and comprehending the way independent variables interact. like pH, Inoculum size, Incubation time, agitation, temperature, and Initial concentration of metal ion for the maximal Cu removal. This model was used to identify the ideal conditions: pH 6.71, 90.61 min of incubation time, 5 ml of inoculum size, and agitation 113 rpm. In these situations, the effectiveness of the removal of copper was achieved by 68.97%. The current study has demonstrated that Pseudomonas aeruginosa JRHM33 is highly efficient at removing Cu from electroplating effluents. As a result, it may be used to treat industrial effluents containing Cu before discharging it into bodies of water, which could spur the development of wastewater remediation technologies that are more effective, economical, and ecologically friendly.

Declarations

ACKNOWLEDGMENTS
None.

CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.

AUTHORS’ CONTRIBUTION
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

FUNDING
None.

DATA AVAILABILITY
All datasets generated or analyzed during this study are included in the manuscript.

ETHICS STATEMENT
This article does not contain any studies on human participants or animals performed by any of the authors.

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