Harnessing the bioremediation potential of indigenous Pseudomonas stutzeri for textile effluent treatment: a mechanistic insight

Abstract

Textile industrial effluent is a significant source of environmental pollution, posing serious risks to human health and ecosystem. Also, textile industry is a major consumer of water, a finite and critical natural resource thereby further aggravating environmental challenges. Current effluent treatment methodologies predominantly rely on chemical processes, which are often hazardous and generate copious amount of sludge as secondary waste which eventually contaminate environment. While microbial bioremediation has been explored in previous studies, these efforts have been largely restricted to laboratory-scale applications, with limited success at industrial-scale implementation. This study focuses on the isolation and characterization of an indigenous bacterial strain, Pseudomonas stutzeri, from textile industries, followed by its optimization for enhanced efficacy. Optimization experiments were conducted to evaluate the effects of key physicochemical parameters, including pH, temperature, carbon, and nitrogen sources, on the strain’s performance under in vitro conditions. Proteomic analysis of Pseudomonas stutzeri under control and effluent-stressed conditions revealed differential protein expression, elucidating the molecular mechanisms underlying its response to stressful conditions. The optimized strain was employed for the treatment of the textile effluent at laboratory scale, followed by industrial-scale trials across multiple sites in Rajasthan, India. Effluent samples collected from in vitro and industrial trials were analyzed using high throughput analysis (UV-Vis and AAS). Comparative analysis indicated that the industrial-scale application achieved significantly higher rates of decolorization, degradation, detoxification and mitigation of hazardous components, including dyes, chemicals, and heavy metal contaminants, compared to laboratory-scale experiments. After on-site industrial trials, the pre- and post-treated effluent was analyzed using FT-IR and GC-MS. The observations from the study provided insights into the functional group transformations and identified degraded metabolites, confirming the biodegradation potential of the screened isolate. This study highlights the untapped potential of Pseudomonas stutzeri as a robust and scalable technology for the bioremediation of dye/chemical/heavy metal loaded textile effluents at industrial scale, hence can be used to promote sustainable development by water upcycling.

Introduction

The discharge of dye- and chemical-laden wastewater from the textile industry is a significant environmental concern in many regions worldwide. Textile industries consume a wide variety of synthetic dyes and chemicals, releasing substantial volumes of highly polluted effluent into the environment. Additionally, the presence of harmful components like heavy metals and chlorinated compounds in these dyes poses severe risks to human health¹. Therefore, the Ministry of Environment and Forest (MoEF) has introduced stringent regulations to mitigate the environmental impacts of industrial wastewater discharge. According to the latest NGT guidelines, textile industries are prohibited from directly discharging untreated effluents into water bodies. Instead, these industries must implement effective effluent treatment systems to ensure that wastewater is treated and reused sustainably. These measures aim to prevent water pollution, conserve resources, and align with the broader goals of environmental sustainability².

Major challenges faced by the textile industry include high water consumption, voluminous effluent generation and inefficient wastewater treatment methods. Industries continue to seek innovative and effective solutions to minimize the environmental damage caused by effluent discharge³. A robust and efficient treatment process is required to address residual dye color, mitigate toxic contaminants, ensure environmental safety and minimize the need for costly post-treatment procedures⁴. Inappropriate disposal of textile dye effluent leads to the release of toxic metabolites that are often carcinogenic, mutagenic, and hazardous to flora and fauna, further aggravating global environmental problems. Due to this, there is an urgent need for cost-effective and eco-friendly technologies to facilitate the biodegradation of these pollutants. Biological processes involving diverse microorganisms, including bacteria, fungi, yeast, and algae, has gained significant attention due to their environmental sustainability, along with added advantage of lower sludge production, and cost-effectiveness. Among these, bacteria are particularly efficient in achieving high levels of dye degradation and complete mineralization under optimal conditions⁵. Recent studies have demonstrated that microbial degradation of textile effluent is more economical and environmentally friendly than traditional physicochemical methods⁶.

This study aimed to identify a potent bacterial strain capable of decolorizing, detoxifying, and degrading textile dyes, industrial effluents containing recalcitrant dyes, chemical contaminants and heavy metals. It also encompasses on the assessment of the in vitro as well as on-site treatment of textile effluent with the definitive process, followed by efficacy analysis using high throughput analytical techniques to evaluate decolourization and detoxification efficiency. Additionally, the evaluation of bacterial stress-responsive proteins as well as enzymes was analyzed to elucidate the underlying mechanisms of dye degradation and detoxification.

Materials and methods

Collection of sample

Effluent samples were collected from various textile hubs in Rajasthan, India (Sanganer, Jaipur, Jodhpur, and Pali) including MSMEs as well as CETPs. The source of effluent was nearby water bodies, drainage canals, and post primary treatment tanks (in CETPs). Also, soil/sludge samples were collected from these industrial sites by excavating up to a depth of 1 m using a sterile shovel. Effluent and soil/sludge samples were stored in the laboratory at 4 °C in sterile glass bottles and zip-lock polybags respectively, until further use⁷.

Isolation of dye degrading bacteria and its identification using 16 S rRNA gene sequencing

Samples (effluent, soil and sludge) were subjected to modified enrichment culture method in order to isolate dye degrading bacterial isolates. This technique involves three steps (i) enrichment, (ii) isolation and, (iii) screening. Firstly, 1 mL (or 1 g) of each sample (effluent, soil or sludge) was inoculated into 9 mL of nutrient broth and kept at 37 °C for 48 h under shaking conditions. After incubation, broth culture was streaked on selective media (MacConkey Agar and Mannitol Salt Agar) containing 100 mg/L of Acid Red 249 dye (C.I. no. 18134; RM9202), a synthetic azo dye procured from Himedia. This dye is widely used in textile industries for colouring variety of fabrics. Also, its environmental persistence and toxicity makes it a suitable model compound for assessing dye degradation potential. Colonies showing remarkable zone of decolorization within 24 to 48 h were picked and sub-cultured on Acid Red enriched media in order to calculate its zone of decolorization. Subsequently, the screened bacterial isolates were inoculated in nutrient broth containing 100 mg/L of Acid red dye and incubated for 24 h at 37 °C under static condition. After 24 h, 2 mL aliquot of culture broth was taken and centrifuged at 10,000 rpm for 10 min, followed by its absorbance measurement at 525.5 nm (λ_max Acid Red) in order to calculate percentage (%) decolorization of Acid Red dye. Isolate showing highest decolourization efficiency was selected and used for further analysis⁶.

The screened bacterial isolate was identified based on its morphological characteristics, Gram staining, and using 16S rRNA sequencing. For molecular identification, the genomic DNA extraction was performed using Phenol Chloroform DNA isolation method⁸. The purity of DNA was measured using 260/280 ratio, followed by PCR amplification of the 16S rRNA gene for bacterial small subunit rRNA. Bacterial DNA templates were amplified with primers 27F (5’-AGA GTT TGA TCM TGG CTC AG-3’) and 1492R (5’-CGG TTA CCT TGT TAC GAC TT-3’). The PCR master mix contained 1x Taq buffer with MgCl₂, 200 µM dNTPs, 50 pmol of each primer, 2.5 U Taq polymerase, and 50 ng DNA template, with the volume adjusted to 25 µL using nuclease-free water. Amplification was performed under the following conditions: initial denaturation at 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min, followed by a final extension at 72 °C for 7 min. The amplified products (~ 1500 bp) were visualized on a 1% agarose gel stained with ethidium bromide and compared against sequences in the NCBI GenBank database using the NCBI BLAST tool (http://www.ncbi.nlm.nih.gov) to taxonomically characterize the bacteria strain⁹. The culture was deposited at NCCS, Pune (MCC 4702) on 22nd April 2021 and sequence was submitted to NCBI database with accession number OM133769.1 on 11th January 2022. Phylogenetic tree was constructed by studying the evolutionary history of isolate using maximum likelihood method, which was performed in MEGA XI software and the phylogenetic tree was drawn using iTOL online software¹⁰.

In vitro optimization of physicochemical parameters

The bacterial ability to decolorize the Acid Red dye (Himedia) under different conditions was investigated using decolorization rate as the index. The influence of various environmental parameters (pH, temperature, carbon and nitrogen sources) on the decolorization of Acid Red dye was studied¹¹.

The screened bacterial isolate was initially cultured in Nutrient Broth (Himedia) at 37 °C for 24 h, followed by measurement of its absorbance at 600 nm to determine the concentration of primary culture in CFU/mL. A uniform cell density of primary culture was then inoculated into 20 mL Nutrient Broth supplemented with 100 mg/L Acid Red dye under varying conditions of pH, temperature, carbon, and nitrogen sources. The culture media was centrifuged and the absorbance of supernatant was measured at 0 h and 24 h, respectively in order to calculate % dye degradation efficiency. A bacterial culture without Acid Red dye served as the reference (blank) during spectrophotometric analysis. The range of parameters used while optimization were as follows: pH range : 3–9; temperature: 30 °C, 37 °C, 40 °C, 45 °C and nitrogen source: Proteose peptone, ammonium dihydrogen phosphate, yeast extract, tryptone at concentration range of 0.5-2%. Similarly, carbon source included glucose, sucrose, maltose, and xylose at same concentration range of 0.5-2%, respectively. Each experiment was performed in triplicate and the mean ± SD values were recorded for analysis.

Where A_i = Initial absorbance at 0 h; A_f = Final absorbance at 24 h.

Protein profiling of bacteria using LCMS

The protein profiling of control and effluent-stressed bacteria helped in providing insights about the induction of stress responsive proteins and cell’s adaptive mechanisms in response to adverse conditions. The bacterial culture was grown in control (Nutrient Broth) and stress (50% diluted effluent in water) conditions for 24 h at 37 °C, followed by whole-cell protein extraction according to the Acetone-SDS method with some modifications as described by Bhaduri & Demchick, 1983¹². After incubation, the culture was used to isolate total protein and the protein concentration was determined using the Lowry assay¹³. Extracted proteins (50–100 µg) were reduced with 10 mM dithiothreitol (DTT) at 37 °C for 1 h and alkylated with 50 mM iodoacetamide (IAA) in the dark for 30 min. Proteins were digested with trypsin (enzyme-to-protein ratio of 1:50) overnight at 37 °C. The digested peptides were desalted using C18 spin columns, dried under vacuum, and reconstituted in 0.1% formic acid for LC-MS analysis. The isolated total protein was further analysed by OHR-LCMS at Sophisticated Analytical Instrument Facility (SAIF), IIT Bombay facility. It was conducted using a Thermo EASY-nLC system coupled with a Q Exactive Plus Orbitrap mass spectrometer. Mobile Phase: solvent A: 0.1% Formic Acid in milliQ water, solvent B: 85:15 (Acetonitrile: milliQ water) + 0.1% Formic Acid Chromatographic separation was performed with a gradient elution at 300 nL/min, using solvent B compositions of 2% (0–5 min), 15% (5–80 min), 45% (80–100 min), 95% (100–105 min), and 2% (105–115 min). Mass spectrometry was operated in positive polarity mode with a scan range of 350–2000 m/z for full MS and 200–2000 m/z for dd-MS², a resolution of 70,000 for full MS and 17,500 for dd-MS², and an AGC target of 1 × 1061 \times 10^61 × 106 for full MS. Data-dependent acquisition targeted the top 15 precursors with an isolation window of 1.2 m/z, normalized collision energy of 27, and a dynamic exclusion of 35 s. Data was processed using Xcalibur software¹⁴.

Treatment of textile industrial effluent under in vitro conditions

To evaluate the efficacy of bacterial isolate at laboratory scale, effluent samples were collected from six textile industries and stored in air tight containers at 4 °C until further use. Bacterial biodegradation of textile industrial effluent was carried out in 250 mL beakers containing 200 mL of effluent containing dyes and chemicals. The effluent was enriched with nutrient media (Beef extract, peptone, sodium chloride supplemented with 1.5 g/L yeast extract and 2 g/L glucose) to support the growth of microorganism that produces enzymes necessary for the treatment.

The microbial culture used for effluent treatment exhibited a concentration of approximately 3.2 × 10⁹ CFU/mL. To determine the optimal inoculum volume, the culture was tested within a range of 20–50 mL per litre of effluent. Based on this, the resulting microbial load in the treated effluent was estimated to be approximately 1.74 × 10¹⁰–1.92 × 10¹⁰ CFU/mL. The effluent was subsequently incubated at room temperature for 9 h to simulate the industrial conditions. Following the incubation period, the effluent was centrifuged and its supernatant was analysed for biodegradation efficacy using UV-Vis spectroscopy and Atomic Absorption Spectroscopy (AAS).

Decolourization assay using UV-Vis spectrometry

The absorbance of untreated and bacteria-treated effluent samples was measured using a UV-Vis spectrophotometer (ELICO SL 159 UV-Visible spectrophotometer). A quartz cuvette with a 1 cm path length was used, and water was used as the blank. Absorbance readings were recorded in the wavelength range of 300–800 nm. The maximum absorbance peak (λ_max) corresponding to the primary dye component in the effluent was identified for subsequent degradation analysis.

Here, the initial absorbance corresponds to the untreated effluent, and the final absorbance corresponds to the effluent after bacterial treatment. All experiments were performed in triplicate to enhance statistical accuracy of results¹⁵.

Detoxification of heavy metal toxicants

The digestion of pre- and post-treated effluent samples for heavy metal analysis was performed according to the U.S. EPA (1996): Method 3050B. Metal concentrations of Cu, Zn, Al, Fe, Cd, Hg, As, Cr, and Pb in the pre- and post-treated effluent were analysed using Atomic Absorption Spectrophotometer (Thermo scientific iCE 3000 AA05124904 v1.30 Atomic Absorption Spectrometer). Calibration standards for each metal were prepared using certified stock solutions (10,000 mg/L), and deionized water was used as the blank. The operating conditions (lamp current, slit width, wavelength, and flame type) were set according to the manufacturer’s specifications for each metal. All experiments were performed in triplicate to ensure replicability¹⁶.

On-site treatment and characterization of textile effluent under industrial conditions

Effluent treatment was performed at industrial scale across six industries (50–500 million litres per day) in Rajasthan, India. The treatment conditions were similar to the optimized condition at laboratory scale (Sect. 2.4). Following the treatment, the effluent was collected from the industries in air tight containers and stored at 4 °C in laboratory until further use. The pre- and post-treated effluent was analysed for various physicochemical parameters as well as spectrophotometric analysis.

Physiochemical parameters of pre- and post-treated effluent

The physiochemical parameters that were analysed in pre and post treated effluent were pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Oil and Grease, Biochemical Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). The parameters are determined using Indian Standard protocols IS 3025 and results are compared with the standards limits set for textile effluent by Ministry of Environment and Forests (MoEF).

Decolourization assay using UV-VIS

The effluent collected after on-site treatment was evaluated for % degradation of dyes and chemicals using UV-Visible spectrophotometer. The method used was identical to the in vitro (refer Sect. 2.4.1.).

Detoxification of heavy metal toxicants

The sample preparation and the measurement of heavy metal concentration of the effluent were performed according to the same protocol used during in vitro treated effluent analysis.

FTIR and GCMS

Pre- and Post-treated effluent samples collected from industries are subjected to centrifugation at 8000 rpm for 10 min to separate the cell biomass from the effluent. The supernatant was collected in the fresh vial, and extracted with ethyl acetate using liquid-liquid extraction technique. Further, the organic layer was separated and dried over anhydrous sodium sulphate. Finally, it was evaporated to dryness with rotary evaporator. The dried samples were re-suspended in HPLC grade methanol followed by filtration with 0.22 μm membrane filter and then subjected to FTIR¹⁷, GCMS analysis¹⁸. In FTIR, samples were analysed using Bruker FTIR Alpha Eco ATRIR Spectrometer, with OPUS spectrum acquisition and processing software in mid infrared region 4000 –600 cm^{- 1} with 16 scan speed. In GCMS, sample was analysed using the GCMS instrument, Thermo GC 1300 and “TSQ 8000 Evo “Triple Quadrupole GC-MS MS SYSTEM with auto sampler Al 1310. Gas Chromatography 1300 fused with a GC column TG-5MS AMINE. The column internal diameter was 250 µm with length 30 m; coated film 0.25 µm. The conditions were as follows: PTV Temp. Program: 70°C, hold 2 min, 10°C/min to 270°C, hold 10 min. Carrier gas helium flow rate 1.0 mL/min, split ratio 1:50. GC was equipped with auto-sampler AI 1300 and sample volume was 1 µL which automatically passed into a mass spectrometer. Mass spectrum analysis was conducted using TSQ8000 with transfer line temperature 280°C and ion source temperature 230°C in EI mode. Mass scan time was 4 min with full Scan MS. Chemical constituent components of the extracts were identified by matching the peaks with Computer NIST MS libraries.

Results

Collection of samples

The effluent released from textile industries contains copious amount of residual dyes that are not bound to the fabric during the dying process. The visual characteristics of textile effluent were predominantly blue, dark green, and black in colour. At the time of collection, the pH was measured to be around acidic to neutral ranging between 6.5 and 7.4, which complies with the permissible limit set by WHO and IS 201:2022. The samples were collected during winter season hence temperature of effluent samples was recorded at 17 °C. The sludge and soil samples collected from the vicinity of textile industries were loaded with dyes and alkaline in nature with pH ranging from 7.5 to 9.

Isolation of dye degrading bacteria and its identification using 16 S rRNA gene sequencing

Six different bacterial colonies were isolated from the effluent and soil samples based on their morphological characteristics on Nutrient Agar (Himedia). Out of these, one isolate showed the best dye degradation efficacy and was further analysed. Growth on selective media (MacConkey agar) and Gram staining confirmed that it was a Gram-negative bacterium (Fig. 1). It had a smooth and short rod shape. Further, the identification of the isolate was confirmed by 16 S rDNA sequencing. Genomic DNA was isolated from the bacterium, and PCR was performed using 27 F and 1492R primers to amplify the 16 S rRNA gene fragment. This was sequenced at NCMR (National Centre of Microbial Resource), Pune. The FASTA sequence of the 16 S rDNA was used to perform BLAST (Basic Local Alignment Search Tool). The 16 S rDNA sequence was submitted to NCBI GenBank as Pseudomonas stutzeri BS106 (Accession number OM133769.1). Phylogenetic analysis of the 16 S rDNA sequence for the screened bacterial isolate was performed based on maximum identity scores. The phylogenetic tree (Fig. 2) showed the phylogenetic relationship between the consensus and the obtained aligned sequences using BLASTn. The phylogenetic tree was constructed using the 16 S rDNA sequences of bacterial isolates, including the screened isolate obtained from textile effluent (highlighted in white text). The tree was generated using the Maximum Likelihood method, incorporating a suitable evolutionary model to determine relationships among the sequences.

Fig. 1.

Photoplate showing (a) morphological (cultured on Mac Conkey Agar media) and (b) microscopic observation (400X) of screened bacterial isolate, Pseudomonas stutzeri.

Fig. 2.

Phylogenetic tree of the 16 S rDNA sequences of the screened bacterial isolate, BS106 for dye decolorization. Maximum Likelihood method with 1000 bootstrap replications was used in the inferred phylogenetic tree. Tree was generated using MEGA XI and designed by iTOL v6.

The isolate of interest clusters within a clade containing closely related sequences, including JF834281.1, JF834289.1, and JF834284.1, suggesting its phylogenetic placement within a specific bacterial genus. Notably, it shares a high degree of similarity with GU594662.1, indicating a close evolutionary relationship. Distinct clustering patterns observed in the tree demonstrate the genetic diversity among the studied isolates, highlighting the evolutionary divergence within the taxa analysed. The placement of the isolate in proximity to the clade containing EU652064.1, HQ731757.1, and FR675680.1 suggests its potential affiliation with a genus closely related to these sequences and might indicate shared metabolic or ecological characteristics relevant to its survival in textile effluent environments. This analysis comprehensively describes the evolutionary lineage of the screened isolate and its potential adaptation mechanisms, which can be further beneficial in pollutant degradation from industrial effluents.

In vitro optimization of pH, temperature, nitrogen source, and carbon source

The results of degradation experiment of Acid Red dye by Pseudomonas stutzeri was evaluated with the effect of range of pH, temperature, carbon and nitrogen sources under aerobic static condition at 37 °C.

Effect of pH on dye degradation.

The degradation efficiency of Pseudomonas stutzeri (strain BS106) was assessed across a pH range of 3–11, as shown in Fig. 3a. The maximum degradation efficiency was observed at neutral pH (6–7); with approximately 100% degradation achieved at both pH 6 and 7. A slightly lower efficiency was noted at pH 5 and pH 8 with 92.03% and 95.19% efficiency, respectively. Beyond this range, degradation efficiency declined significantly, with the lowest values observed at extreme acidic (pH 3, 17.07%) and alkaline (pH 11, 22.8%) conditions. The results indicate that Pseudomonas stutzeri performs optimally in a neutral to slightly acidic pH environment.

Fig. 3.

In vitro optimization of culture conditions (pH, temperature, carbon, and nitrogen sources) for degradation analysis of Acid Red dye by bacterial isolate, Pseudomonas stutzeri.

Effect of temperature on dye degradation.

The temperature optimization study for the degradation efficiency of Pseudomonas stutzeri (BS106) reveals that the bacterial strain exhibits the highest degradation efficiency at 37 °C. At 40 °C, the degradation efficiency slightly declines to 80.87%, indicating that while the strain retains substantial activity at this elevated temperature, it is less effective than at 37 °C. At 30 °C, the degradation efficiency drops to 62.91%, suggesting reduced metabolic or enzymatic activity at lower temperatures. The lowest degradation efficiency, 29.46%, was observed at 45 °C, likely due to thermal stress impairing bacterial growth or enzyme stability (Fig. 3b).

Effect of nitrogen source on dye degradation.

Among the four organic and inorganic nitrogen sources that are used as co-metabolites to boost the Acid Red dye degradation percentage, yeast extract exhibited the best activity (at pH 7 and 37 °C) across all the concentrations, with maximum of 97.53% at 2 g/L concentration. It was followed closely by proteose peptone, which showed degradation efficiency of 71.39% at optimum concentration of 1.5 g/L. Whereas, tryptone exhibited moderate performance at optimum concentration (1.5 g/L) and thereby dropped the percent dye degradation significantly (40.83%) at 2 g/L concentration. In contrast, Ammonium Dihydrogen Phosphate (ADP) rather acted as inhibitor for the bacterial efficacy. At all concentrations, it showed the least dye degradation potential with the range of merely 1.7% − 3.8% (Fig. 3c).

Effect of carbon source on dye degradation.

The carbon source optimization study for Pseudomonas stutzeri demonstrated that xylose and glucose were the most effective carbon sources at pH 7 and 37 °C, with degradation efficiencies increasing consistently as their concentrations rose from 0.5 g/L to 2 g/L. Xylose exhibited the highest efficiency at 2 g/L (66.72%), closely followed by glucose (74.43%). In contrast, sucrose and maltose showed moderate efficiencies, with sucrose peaking at 1 g/L (47.16%) and declining thereafter. Similarly, maltose showed a maximum efficiency of 47.32% at 1 g/L but plateaued at higher concentrations. Overall, it was observed that the bacteria was performing better (≥ 95% efficacy) without carbon source additives (Fig. 3d).

Protein profiling of bacteria using LCMS

The heatmap (Figs. 4 and 5a and b) analysis revealed the differential expression of several proteins in Pseudomonas stutzeri under effluent stress compared to control conditions, highlighting significant metabolic reorganization and stress adaptation mechanisms. Proteins such as Hsp20/alpha crystallin family protein and chaperonin GroEL were notably upregulated, reflecting an increased requirement for protein refolding as well as stabilization under toxic conditions. The selective upregulation of certain isoforms of GroEL, while others were downregulated, indicates response to maintain proteostasis. RND family efflux transporters were highly upregulated, suggesting their critical role in detoxifying harmful chemicals and pollutants present in the effluent. Similarly, TonB-dependent receptors exhibited elevated expression, likely to enhance nutrient acquisition and resistance to heavy metal stress. Proteins involved in heavy metal resistance, such as copper resistance system multi-copper oxidase, were also upregulated, showcasing the bacterial adaptation to mitigate metal toxicity through oxidation-reduction mechanisms.

Fig. 4.

LC-MS protein profiling of Pseudomonas stutzeri (BS106) under control and stress conditions. (a) represents the protein profile under control conditions, (b) represents the profile under effluent stress conditions, exhibiting altered peak intensities and retention patterns indicative of stress-induced proteomic changes.

Fig. 5.

Heatmap illustrating differential protein abundance in bacterial cells under control and textile dye effluent–induced stress conditions. The heatmap presents a comparative profile of bacterial proteins detected under control and stress (dye-contaminated effluent) conditions, based on LC-MS analysis. Each row represents a unique protein, while columns indicate the experimental conditions. Each column pair displays protein abundance for the respective condition, highlighting metabolic and stress-responsive adaptations. Protein abundance values were normalized and transformed to a log₁₀ scale, as shown by the color bar on the right. The color gradient ranges from purple (log₁₀ abundance = 0) to red (log₁₀ abundance ≥ 7), corresponding to increasing protein abundance.

Key enzymes of the arginine metabolism pathway, including arginine deiminase and arginine-ornithine antiporter, were upregulated, potentially contributing to pH homeostasis and energy production during stress. Additionally, histidine kinase and uncharacterized zinc-type alcohol dehydrogenase exhibited higher expression, indicating their roles in environmental sensing, signalling, and detoxification of stress-inducing organic compounds. Phosphoglycerate kinase, a glycolytic enzyme, showed increased abundance, pointing towards a metabolic shift to glycolysis to meet elevated ATP demands during stress.

Conversely, several proteins were significantly downregulated under effluent stress, reflecting energy conservation and resource reallocation. Ribosomal proteins such as uS4 and uS11, along with elongation factors Tu and G, exhibited reduced expression, indicating suppressed translation and protein synthesis. Outer membrane proteins, including porins like OmpA, were downregulated, suggesting decreased membrane permeability to limit the influx of toxic compounds. ATP synthase subunits were also repressed, indicating a reduced reliance on oxidative phosphorylation and a shift towards alternative energy-generating pathways. Enzymes involved in central metabolism, such as alcohol dehydrogenase and citrate synthase, showed decreased abundance, supporting the idea of metabolic reorganization under stress. Proteins related to polysaccharide biosynthesis were also downregulated, suggesting a shift away from biofilm matrix production as part of the adaptive response.

It has also been observed that there was upregulation of oxidative stress proteins, such as Superoxide Dismutase (SOD) and catalase, suggests a pronounced activation of oxidative stress response mechanisms. Superoxide dismutase also has metal binding property. It might be correlated with heavy metal mitigation in effluent. It also implies increased expression of ROS-detoxifying enzymes under stress conditions to neutralize reactive oxygen species (ROS) generated due to exposure to toxic pollutants. Interestingly, proteins related to energy metabolism, particularly those in the TCA cycle and glycolytic pathways, such as isocitrate dehydrogenase and succinate dehydrogenase were also upregulated, indicating heightened energy production. It supports the operation of energy-demanding processes, including efflux systems and stress-related protein synthesis.

Additionally, the downregulation of proteins related to flagellar synthesis and bacterial motility suggests reduced energy investment in cellular movement. Flagellar proteins like flagellin and motor components are often repressed under stress conditions.

Treatment of textile industrial effluent under in vitro conditions

Effluent samples were collected from six textile industries and treatment with Pseudomonas stutzeri was performed under in vitro conditions. It was concluded that the addition of 6 mL of actively growing microbial culture to 200 mL of effluent resulted in efficient treatment. The pre- and post-treated effluent samples were further tested for various parameters to provide the insights into decolorization, degradation and detoxification of recalcitrant (dyes, chemicals, and heavy metal toxicants).

Decolourization assay using UV-VIS spectrometry

The UV-Vis spectrophotometric analysis of effluent samples by the screened bacterial isolate, Pseudomonas stutzeri revealed substantial degradation of dyes in the range 30.33% to 98.16% respectively. There was significant reduction in absorbance of samples after treatment (Fig. 6). The λ_max range for six effluent was measured and found to range between 325 and 347 nm, indicating the presence of aromatic and azo compounds. Under in vitro conditions, the percentage degradation of organic compounds ranged from 30.33% (SA101) to 69.65% (JY101) and dye decolorization ranged from 84.47% (SA101) to 98.17% (VS101). The degradation percentage for each sample is shown in Table 1.

Fig. 6.

Photoplate showing the UV-Vis spectrophotometric analysis of pre- (red) and post-treated (green) effluent samples of various textile industries. The treatment was performed with Pseudomonas stutzeri under controlled in vitro laboratory conditions.

Table 1.

Table showing λ_max of various effluent samples collected from textile industries and their % dye degradation by Pseudomonas stutzeri under in vitro and on-site conditions.

Sample	λmax (200−400)	% Degradation		λmax (400−800)	% dye decolorization
		In vitro	On-site		In vitro	On-site
VS101	334	40.078	51.613	621	98.166	90.792
RA101	325	39.508	96.845	400	69.513	77.879
SA101	334	30.338	81.962	400	84.478	75.338
JY101	347	69.647	72.226	400	86.125	79.072
VP101	328	59.324	97.064	400	84.647	88.625
DI101	342	39.508	85.576	419	84.713	87.414

Detoxification of heavy metal toxicants

The mitigation of heavy metal toxicants from textile industrial effluents using screened bacterial isolate, Pseudomonas stutzeri was evaluated using Atomic Absorption Spectroscopy (AAS). The results observed after in vitro treatment under optimized conditions demonstrated significant reduction in heavy metal concentrations (viz. Copper, Aluminium, Zinc, Iron, Cadmium, Mercury) of all effluent samples (Table 2; Fig. 7). Among the metals, Copper (Cu), Iron (Fe) and Zinc (Zn) exhibited high detoxification rate. Also, it was found that Arsenic (Ar), Chromium (Cr) and lead (Pb) were absent in effluent. However, in case of iron, maximum mitigation was observed in DI101 (99.9%), VP101 (98%), VS101 (91.14%), and JY101 (90%), respectively. Whereas in case of copper (Cu), it ranges from 59.16% to 94.54% based on different effluent samples. Similarly, there was significant reduction in Aluminium (Al) concentration in post-treated samples. RA101 and JY101 exhibited maximum 100% and 96% detoxification, respectively. Conversely, Cadmium (Cd) showed considerably lower reduction rate i.e. 3.77% to 29.93% only. In this study, Mercury (Hg) detoxification range is broad. The effluent sample SA101 exhibited highest mercuric mitigation with 84% detoxification whereas in other samples (RA101 and DI101), it is only 15.74% and 8.16%, respectively.

Table 2.

Heavy metal concentration in untreated and bacteria-treated textile industrial effluent under in vitro condition. Data are expressed as mean ± SD. BDL indicates values below the detection limit.

Heavy metal	Treatment	Concentration in effluent samples (mg/L)
		SA101	VP101	VS101	RA101	JY101	DI101
Copper	Pre-treated	0.487 ± 0.025	5.245 ± 0.010	7.701 ± 0.048	0.222 ± 0.001	1.579 ± 0.024	1.521 ± 0.009
	Post-treated	0.198 ± 0.005	0.286 ± 0.018	0.751 ± 0.029	0.081 ± 0.007	0.322 ± 0.006	0.169 ± 0.036
Zinc	Pre-treated	0.498 ± 0.021	5.658 ± 0.010	10.001 ± 0.045	0.932 ± 0.018	1.898 ± 0.458	1.259 ± 0.081
	Post-treated	0.112 ± 0.009	1.764 ± 0.049	4.012 ± 0.996	0.134 ± 0.017	0.537 ± 0.007	0.379 ± 0.023
Aluminium	Pre-treated	9.278 ± 0.487	7.542 ± 0.558	1.257 ± 0.854	6.325 ± 0.977	13.254 ± 1.264	13.485 ± 1.261
	Post-treated	7.832 ± 0.259	0.377 ± 0.007	0.396 ± 0.856	BDL	0.530 ± 0.010	2.843 ± 1.003
Iron	Pre-treated	0.997 ± 0.789	1.004 ± 0.795	2.485 ± 0.125	2.545 ± 0.063	9.255 ± 0.387	3.225 ± 0.114
	Post-treated	0.603 ± 0.010	0.020 ± 0.006	0.22 ± 0.004	2.233 ± 0.017	0.925 ± 0.977	0.003 ± 0.007
Cadmium	Pre-treated	0.854 ± 0.045	0.109 ± 0.010	0.689 ± 0.010	1.254 ± 0.012	1.022 ± 0.076	0.889 ± 0.007
	Post-treated	0.768 ± 0.020	0.101 ± 0.045	0.662 ± 0.085	0.878 ± 0.451	0.909 ± 0.088	0.723 ± 0.562
Mercury	Pre-treated	19.625 ± 1.251	35.261 ± 6.254	36.264 ± 2.547	45.658 ± 4.215	84.258 ± 0.795	21.267 ± 1.799
	Post-treated	3.141 ± 0.236	12.341 ± 2.516	23.203 ± 4.256	38.470 ± 0.759	46.139 ± 2.365	19.530 ± 8.689

Fig. 7.

Graphical representation of the percentage detoxification of heavy metals from textile industrial effluents (i) SA101, (ii) VP101, (iii) VS101, (iv) RA101, (v) JY101, (vi) DI101 achieved by Pseudomonas stutzeri under in vitro conditions.

Treatment of textile industrial effluent under on-site conditions and its characterization

Physicochemical parameters of pre- and post-treated effluent

Textile effluents are loaded with various dyes and chemicals used throughout industrial processes. These recalcitrants alter the physico-chemical properties of water, including BOD, COD, TDS, TSS, pH, oil and grease. The comparative characteristics of pre- and post-treated textile industrial effluent are shown in Table 3. The treatment with Pseudomonas stutzeri reduces the pH of the effluent to near neutral range (7–8) and also reported 4.38–86.93% reduction in TDS. However, sample RA101 reported the increase in TDS as it might be possible due to the dissolution of suspended solids under specific pH conditions, hence leading to the increase in TDS. The COD and BOD of the pre-treated effluent was very high whereas after treatment, it was significantly lower and within compliance limit set by MoEF as 250 mg/L and 50 mg/L, respectively. Similarly, a clear difference was observed in the oil and grease content between pre- and post-treated samples.

Table 3.

Physico-chemical parameters of Raw and bacteria treated textile industrial effluent.

S. no.	Sample	Treatment	Physicochemical parameters
			Total dissolved solids (TDS)	Total suspended solids (TSS)	Chemical oxygen demand (COD)	Biochemical oxygen demand (BOD)	PH	Oil and grease
Standard Permissible limits by MoEF			2100 mg/L	100 mg/L	250 mg/L	50 mg/L	5.5-9.0	10 mg/L
1	VS101	Pre-treated	2376	106	626	115	7.06	3
		Post-treated	2272	27	267	51	6.93	2
2	RA101	Pre-treated	5296	548	1374	243	12.05	5
		Post-treated	7020	3356	1198	218	9.99	5
3	SA101	Pre-treated	9278	266	1432	289	7.99	4
		Post-treated	1213	50	136	23	7.35	1
4	JY101	Pre-treated	4948	428	321	53	6.88	3
		Post-treated	4571	56	168	26	6.63	2
5	VP101	Pre-treated	3452	910	1927	366	12.81	7
		Post-treated	2484	620	298	51	7.02	2
6	DI101	Pre-treated	8938	328	194	51	8.25	1
		Post-treated	2868	18	23.7	4.9	7.12	BDL

Decolourization assay using UV-Vis spectrometry

The UV-Vis spectrophotometric analysis of textile effluent samples treated with screened bacteria at industrial scale gave exemplary results as evidenced by reduction in absorbance across UV-Vis range (Fig. 8). After on-site treatment of effluent at industrial scale capacity, the % degradation across ultraviolet and visible range was recorded up to 97%. In comparison with in vitro trials, reduction (%) across UV range (200–400 nm) was significantly higher than visible range (380–700) as shown in Table 1.

Fig. 8.

Photoplate illustrating UV-Vis spectrophotometric analysis of pre- (red) and post-treated (blue) effluent samples collected after treatment at textile industries.

Detoxification of heavy metal toxicants

In textile industries, effluent was treated with Pseudomonas stutzeri under optimized condition and the collected effluent was tested for heavy metal concentration in laboratory using AAS. The results showed significant reduction in the heavy metal concentration after its treatment with screened isolate (Table 4; Fig. 9). In comparison to in vitro treatment, the results of on-site treatment were higher i.e. 100% detoxification was achieved for several metals notably copper, aluminium, iron and mercury. This study reflects the higher efficiency of bacterial treatment at industrial scale. For copper, reduction (%) in concentration was observed from 79.17% (SA101) to 100% (VP101). Zinc detoxification was similarly high, with efficiencies exceeding 97% in most samples. Complete detoxification of aluminium and iron was observed in samples like VP101, RA101 and JY101, indicating the robust metabolic machinery of bacteria to mitigate these metals. However, cadmium and mercury showed variable reduction rates. Mercury removal was observed highest in SA101 and VP101 (100%) but significantly lower in samples such as DI101 (16.02%). Cadmium detoxification followed similar trend, showing 29.93%, 18.69% in RA101 and DI101, respectively. In other samples (VP101, VS101, SA101, JY101), it was showing less than 10% detoxification. After the treatment process, the effluent was separated from the sludge. The heavy metals were found to be effectively mitigated from the effluent and concentrated within the settled sludge. AAS analysis confirmed high concentrations of these metals in the post-treatment sludge, rendering it biohazardous, as detailed in patent document¹⁹ (Patent Number: 556875).

Table 4.

Heavy metal concentration in untreated and bacteria-treated textile effluent at industrial site. Data are expressed as mean ± SD. BDL indicates values below the detection limit.

Heavy metal	Treatment	Concentration in effluent samples (mg/L)
		SA101	VP101	VS101	RA101	JY101	DI101
Copper	Pre-treated	0.468 ± 0.036	4.813 ± 0.008	6.721 ± 0.093	0.322 ± 0.008	3.579 ± 0.022	0.521 ± 0.001
	Post-treated	0.098 ± 0.004	BDL	0.118 ± 0.071	0.054 ± 0.020	0.015 ± 0.004	0.027 ± 0.024
Zinc	Pre-treated	0.498 ± 0.036	5.613 ± 0.008	6.721 ± 0.093	0.322 ± 0.008	3.579 ± 0.022	0.521 ± 0.001
	Post-treated	0.007 ± 0.004	0.009 ± 0.034	0.142 ± 0.106	0.059 ± 0.013	0.015 ± 0.004	0.027 ± 0.024
Aluminium	Pre-treated	7.290 ± 0.546	5.412 ± 0.436	2.902 ± 0.711	4.287 ± 0.918	11.132 ± 4.350	12.932 ± 2.611
	Post-treated	5.412 ± 0.436	BDL	1.466 ± 0.724	BDL	BDL	8.139 ± 1.398
Iron	Pre-treated	0.767 ± 0.379	1.170 ± 0.005	2.264 ± 0.005	1.088 ± 0.027	5.040 ± 0.387	0.987 ± 0.284
	Post-treated	0.364 ± 0.005	BDL	BDL	BDL	BDL	BDL
Cadmium	Pre-treated	0.344 ± 0.030	0.291 ± 0.006	0.299 ± 0.010	0.362 ± 0.011	0.300 ± 0.056	0.294 ± 0.007
	Post-treated	0.302 ± 0.138	0.260 ± 0.001	0.269 ± 0.017	0.181 ± 0.160	0.291 ± 0.004	0.180 ± 0.127
Mercury	Pre-treated	14.477 ± 17.539	29.255 ± 5.778	24.073 ± 4.676	30.467 ± 0.673	26.949 ± 0.852	48.829 ± 1.999
	Post-treated	BDL	BDL	20.217 ± 6.436	22.624 ± 0.484	17.452 ± 9.017	48.749 ± 1.691

Fig. 9.

Graphical representation of the percentage detoxification of heavy metals from textile industrial effluents (i) SA101, (ii) VP101, (iii) VS101, (iv) RA101, (v) JY101, (vi) DI101 achieved by Pseudomonas stutzeri at on-site industrial conditions.

High throughput analysis of textile industrial effluent (FT-IR and GC-MS)

FT-IR and GC-MS analysis was carried out for textile effluent before and after treatment with screened bacterial isolate, Pseudomonas stutzeri. The process of degradation of dyes and chemicals present in effluent is depicted either by loss of spectral peaks or by formation of new peaks in infrared spectrum (Fig. 10). The pre- and post- treated effluent samples were collected from six textile industries and analysed to study the degradation of toxic compounds by bacteria. The detailed analysis of functional group alterations after the treatment evaluated using FT-IR spectroscopy is mentioned in Table 5. The GC-MS analysis of effluent before treatment revealed the presence of complex organic molecules that are components of dyes and chemicals commonly used in industries. The reduction of C = C and C–H bonds in complex aromatic compounds like 3-Benzylsulfanyl-3-fluoro-2-trifluoromethyl-acrylonitrile, phthalates, phenols leads to the formation of simpler derivatives as toluene and cresotic acid derivatives. It is because of the hydroxylation or cleavage of aromatic rings by dioxygenases. The presence of phthalic acid esters, tetradecanoic acid, octadecanoic acid, and benzenepropanoic acid were also detected in pre-treated effluent. These compounds might have been hydrolysed by esterases and amidases to form smaller chain compounds or CO₂ due to reduction or disappearance of C = O/C–O stretching peaks in infrared spectrum via beta-oxidation pathway. The conversion of halogenated compounds like Octatriacontyl pentafluoropropionate to 3-Benzylsulfanyl-3-fluoro-2-trifluoromethyl-acrylonitrile (present in post treated effluent) is a multi-step process. It might involve initial hydrolysis to form pentafluoropropionic acid and a long chain alcohol, followed by action of monooxygenases, dehydrogenases, and nitralases. The significant decrease in Retention Time (RT) of compound from 31.89 to 3.38 depicted the decrease in toxicity of post treated effluent due to reduction in molecular size, complexity and removal of bulky functional groups. There was also significant decrease in alkane complexity as dodecane, 2,6,11-trimethyl-, heptacosane, and octadecanes are broken down to form simpler chains. However, siloxanes (octasiloxanes and hexasiloxanes are the only compounds that remained persistent even after the bacterial treatment hence found to be resistant to microbial degradation. another important observation was reduction of nitrile containing compounds i.e. 3-benzylsulfanyl-3-fluoro-2-trifluoromethyl-acrylonitrile. Bacteria have tendency to perform hydrolytic deamination leading to formation of intermediate amides followed by carboxylic acid formation. This is supported by disappearance of C ≡ N stretching peaks (2200–2100 cm^{- 1}) in IR spectrum, leading to degradation and detoxification of textile industrial effluent. This reflects a reduction in toxicity, likely resulting from the degradation of complex, high-molecular-weight compounds and the cleavage of bulky or reactive functional groups. Such structural simplification indicates effective treatment, leading to the formation of less hazardous end products. Overall, the findings emphasize enhanced safety and environmental compatibility of the effluent. The gas chromatograms of pre- and post-treated effluent samples are depicted in Fig. 11.

Fig. 10.

FT-IR spectral analysis of pre-treated (orange) and post treated (blue) textile effluent samples from various industries (a) VP101, (b) RA101, (c) SA101, (d) JY101, (e) VA101, and (f) DI101.

Table 5.

Spectral peaks and their interpreted functional group alterations in the effluent following treatment with the screened bacterial isolate, Pseudomonas stutzeri.

Sample	Wavenumber (cm− 1)	Pre-treated effluent	Post-treated effluent	Functional group and interpretation	References
DI101	3400–3300	Broad, strong peak attributed to O–H stretching in hydrogen-bonded hydroxyl groups and/or N–H stretching.	Reduced intensity, suggesting degradation of phenolic or amino compounds.	O–H and N–H stretching (hydroxyl groups in phenols, alcohols, or amines).	Meenatchisundaram et al.20; Khan et al.21; Rajhans et al.22; Khan and Joshi,23
	3146–3100	Weak peak possibly corresponding to aromatic C–H stretching.	Absent; indicates reduction in aromatic hydrocarbons.	Aromatic C–H stretching. Likely due to dye components.	Rathour et al.24
	2960–2850	Weak peaks attributed to aliphatic C–H stretching (asymmetric and symmetric vibrations).	No significant changes observed.	Aliphatic C–H stretching (–CH₂, –CH₃ groups in hydrocarbons).	Meenatchisundaram et al.20; Mishra et al.25; Thampraphaphon et al.26
	2270–2200	Weak peak assigned to C ≡ N stretching (nitriles).	Intensity reduced, indicating possible degradation.	Nitriles (C ≡ N), which may arise from dye degradation products.	Rathour et al.24; Khan and Joshi,23
	1740–1710	Strong peak characteristic of C = O stretching in carbonyl groups (esters, aldehydes, or carboxylic acids).	Reduced intensity post-treatment, suggesting breakdown of carbonyl-containing compounds.	Carbonyl groups (esters, carboxylic acids, or aldehydes).	Khan et al.21; Thanavel et al.27
	1697–1650	Moderate peak assigned to C = O stretching in amides or conjugated ketones.	Intensity reduced; likely bacterial degradation of conjugated carbonyls or amide structures.	Amides or conjugated ketones, commonly found in dyes or effluent byproducts.	Meenatchisundaram et al.20; Banerjee et al.28
	1610–1510	Broad, strong peaks corresponding to C = C stretching in aromatic rings.	Reduced intensity, indicating partial breakdown of aromatic structures.	Aromatic C = C vibrations, typical of dye molecules.	Khan et al.21; Mishra et al.24; Rathour et al. 24
	1460–1400	Moderate peaks attributed to C–H bending vibrations in aliphatic hydrocarbons.	Decreased intensity post-treatment, suggesting hydrocarbon degradation.	Aliphatic hydrocarbons (C–H bending).	Banerjee et al.28; Thanavel et al.27; Khan and Joshi,23
	1375–1300	Weak peaks attributed to C–H bending in methyl groups (–CH₃).	Intensity decreased, suggesting breakdown of methyl-containing structures.	Methyl (–CH₃) bending, likely from organic pollutants.	Mishra et al.25; Rathour et al.24; Banerjee et al.28
	1230–1000	Multiple strong peaks corresponding to C–O stretching in esters, ethers, and alcohols.	Significant reduction in intensity post-treatment.	C–O stretching vibrations in oxygenated compounds.	Meenatchisundaram et al.20; Mishra et al.25
	900–700	Peaks assigned to out-of-plane C–H bending in aromatic rings.	Reduced intensity, suggesting breakdown of aromatic hydrocarbons.	Aromatic C–H bending vibrations.	Meenatchisundaram et al.20; Khan et al.21; Thanavel et al.27; Thampraphaphon et al.26
JY101	3855–3750	Weak peaks due to free O–H stretching.	Reduced significantly or absent.	Free hydroxyl groups (–OH); possible degradation of simple alcohols or phenols.	Khan et al.21; Banerjee et al.28
	3741–3300	Broad O–H/N–H stretching vibrations.	Formation of new strong peak at 3741 cm-1. Reduced intensity post-treatment.	Hydrogen-bonded hydroxyl or amine groups (phenols, alcohols, or amines). Indicates bacterial degradation of these groups and formation of free hydroxyl groups (O-H)	Meenatchisundaram et al.20; Mishra et al.25; Banerjee et al.28; Rajhans et al.22; Khan and Joshi,23
	2926	Absent in pre treated effluent	New, moderate-intensity peak.	C–H stretching in aliphatic hydrocarbons. Indicates possible formation of simpler aliphatic hydrocarbon chains.	Mishra et al.25; Banerjee et al.28
	2922–2850	Weak peaks for C–H stretching (aliphatic hydrocarbons).	No significant change observed.	Aliphatic C–H stretching; these groups remain largely unaltered.	Mishra et al.25; Thampraphaphon et al.26
	2358.9	Weak peak corresponding to CO₂ stretching.	strong intensity peak present in post-treated spectrum.	Possible CO₂ formation	Rajhans et al.22
	1741–1710	Weak peak in pre treated effluent	Strong peak for C = O stretching in carbonyl groups (esters, aldehydes, carboxylic acids).	Carbonyl groups; bacterial breakdown of esters, carboxylic acids, or aldehydes.	Thanavel et al.27; Thampraphaphon et al.26
	1690–1650	Peak for C = O stretching in amides or conjugated carbonyls.	Reduced in intensity post-treatment.	Amides or conjugated ketones; degradation of nitrogen-containing compounds or conjugated systems.	Banerjee et al.28
	1619–1515	Strong peaks for C = C stretching in aromatic rings.	increased intensity post-treatment.	Aromatic compounds (e.g., dyes); indicates partial breakdown of aromatic structures.	Khan et al.21; Banerjee et al.28; Khan and Joshi,23
	1457	Moderate peak for C–H bending in aliphatic hydrocarbons.	Slight reduction in intensity.	Aliphatic hydrocarbons; minor degradation of methyl or methylene groups.	Mishra et al.25; Banerjee et al.28
	1208–1000	Strong peaks for C–O stretching in esters, ethers, or alcohols.	Significant reduction in intensity.	C–O functional groups; degradation of oxygen-containing compounds (esters, ethers, or alcohols).	Meenatchisundaram et al.20; Rathour et al.24; Thampraphaphon et al.26
	680–600	Weak peaks for C–H out-of-plane bending in aromatic rings.	increased significantly post-treatment.	Aromatic bending; changes in the substitution pattern of the aromatic rings	Meenatchisundaram et al.20; Khan et al.21; Khan and Joshi,23
RA101	3984–3705	Weak peaks corresponding to free O–H stretching vibrations.	Reduced significantly or absent.	Free hydroxyl groups (–OH); degradation of simple alcohols or phenols.	Banerjee et al.28
	3600–3200	Broad O–H and N–H stretching vibrations.	Spilling into smaller peaks with reduced intensity in post-treatment.	Hydrogen-bonded hydroxyl groups (e.g., phenols, alcohols) and amines degraded.	Meenatchisundaram et al.20; Rajhans et al.22; Khan and Joshi,23
	2948–2800	Weak peaks due to aliphatic C–H stretching.	Retained with minor changes in intensity.	Aliphatic hydrocarbons remain mostly unaltered.	Mishra et al.25; Thanavel et al.27
	2397	Weak peak corresponding to CO₂ or nitriles (C ≡ N stretching).	Absent in post-treated spectrum.	Possible degradation of nitrile-containing compounds.	Rajhans et al.22
	1813	Strong, sharp peak indicating C = O stretching in esters or carboxylic acids.	Significantly reduced intensity.	Degradation of carbonyl-containing compounds such as esters or acids.	Thampraphaphon et al.26
	1600–1500	Moderate peaks due to aromatic C = C stretching vibrations.	increased intensity.	Partial breakdown/ conformation change of aromatic rings, common in dye molecules.	Mishra et al.25; Rathour et al.24; Khan and Joshi,23
	1452–1437	Moderate peaks due to C–H bending vibrations in aliphatic hydrocarbons.	Slightly reduced intensity.	Minor degradation of aliphatic hydrocarbons.	Banerjee et al.28; Khan and Joshi,23
	1027	Strong peak corresponding to C–O stretching vibrations in esters, ethers, or alcohols.	Reduced significantly post-treatment.	Breakdown of oxygen-containing compounds such as ethers or esters.	Meenatchisundaram et al.20; Mishra et al.25; Rathour et al.24; Thampraphaphon et al.26
	645–600	Weak peaks for C–H out-of-plane bending in aromatic rings.	Reduced intensity or absent.	Degradation of aromatic structures.	Khan et al.21
SA101	3848–3778	Weak peaks corresponding to free O–H stretching vibrations.	Splitting of peak in post-treated spectrum.	Free hydroxyl groups (–OH); degradation of simple alcohols or phenols.	Banerjee et al.28
	3737–3191	Broad O–H and N–H stretching vibrations.	Reduced intensity in post-treatment.	Hydrogen-bonded hydroxyl or amine groups degraded (phenols, alcohols, or amines).	Meenatchisundaram et al.20; Mishra et al.25; Rajhans et al.22; Khan and Joshi,23
	2948–2800	Weak peaks corresponding to C–H stretching in aliphatic hydrocarbons.	No significant changes.	Aliphatic hydrocarbons remain largely unaltered.	Mishra et al.25; Banerjee et al.28
	2402–2382	Weak peaks possibly associated with C ≡ N or CO₂ stretching.	Absent in post-treated spectrum.	Indicates the degradation of nitriles or removal of CO₂.	Rajhans et al.22
	1735–1725	Strong peak characteristic of C = O stretching in esters or carboxylic acids.	Significantly reduced post-treatment.	Degradation of esters, carboxylic acids, or other carbonyl-containing compounds.	Thanavel et al.27; Khan and Joshi,23
	1618–1516	Strong peaks corresponding to C = C stretching in aromatic rings.	Reduced intensity post-treatment.	Partial breakdown of aromatic compounds, such as those in dyes.	Khan et al.21; Rathour et al.24
	1253–1031	Moderate peaks for C–O stretching vibrations (ethers, esters, or alcohols).	Reduced significantly post-treatment.	Breakdown of oxygen-containing compounds, such as ethers or esters.	Meenatchisundaram et al.20; Mishra et al.25; Thampraphaphon et al.26
	873–603	Weak peaks corresponding to aromatic C–H out-of-plane bending.	Reduced or absent in post-treatment.	Degradation of aromatic structures.	Mishra et al.25; Rathour et al.24; Khan and Joshi,23
VP101	3848–3770	Weak peaks indicating free O–H stretching.	similar intensity in post-treated spectrum.	Free hydroxyl groups (–OH)	Banerjee et al.28
	3737–3190	Broad O–H/N–H stretching, strong in intensity.	slightly increased intensity post-treatment.	Hydrogen-bonded hydroxyl (phenols, alcohols) and amine groups degraded.	Meenatchisundaram et al.20; Rajhans et al.22; Khan and Joshi,23
	2981–2850	Moderate peaks corresponding to aliphatic C–H stretching.	Retained with minimal change.	Aliphatic hydrocarbons remain largely unaltered.	Mishra et al.25; Thanavel et al.27; Thampraphaphon et al.26
	2368–2338	Weak peaks corresponding to C ≡ N or CO₂ stretching.	Absent in post-treated spectrum.	Degradation of nitriles (C ≡ N) or removal of CO₂ during bacterial activity.	Rajhans et al.22
	1742–1725	Strong C = O stretching in esters or carboxylic acids.	Reduced significantly.	Breakdown of carbonyl-containing compounds (esters, acids).	Thampraphaphon et al.26
	1693–1650	Moderate peak for C = O stretching in amides or conjugated carbonyls.	Reduced in intensity.	Degradation of amides or conjugated ketones.	Banerjee et al.28
	1618–1516	Strong peaks indicating C = C stretching in aromatic rings.	Reduced slightly post-treatment.	Breakdown of aromatic compounds, common in dyes.	Khan et al.21; Banerjee et al.28; Khan and Joshi,23
	1463–1431	Weak peaks for C–H bending vibrations.	Slightly reduced.	Minor degradation of aliphatic hydrocarbons.	Mishra et al.25
	1253–1031	Strong peaks for C–O stretching in esters, ethers, or alcohols.	Reduced significantly.	Breakdown of oxygen-containing compounds like esters or ethers.	Meenatchisundaram et al.20; Rathour et al.24; Thampraphaphon et al.26
	873–600	Peaks for C–H out-of-plane bending in aromatic rings.	intensity Reduced significantly or absent.	Degradation of aromatic rings.	Meenatchisundaram et al.20; Khan et al.21; Khan and Joshi,23
VS101	3865–3774	Weak peaks corresponding to free O–H stretching.	Reduced significantly or absent.	Free hydroxyl groups (–OH); degradation of alcohols or phenols.	Banerjee et al.28
	3742–3100	Broad O–H and N–H stretching, strong intensity.	Reduced significantly post-treatment.	Hydrogen-bonded hydroxyls (phenols, alcohols) and amines degraded.	Meenatchisundaram et al.20; Rajhans et al.22; Thanavel et al.27
	2948–2850	Moderate peaks for aliphatic C–H stretching.	Retained with minimal change.	Aliphatic hydrocarbons remain largely unaltered.	Mishra et al.25; Thampraphaphon et al.26
	2398–2338	Weak peaks corresponding to C ≡ N or CO₂ stretching.	Absent in post-treated spectrum.	Breakdown of nitrile-containing compounds (C ≡ N) or release of CO₂.	Mishra et al.25; Rajhans et al.22
	1742–1712	Strong C = O stretching in esters or carboxylic acids.	Reduced significantly post-treatment.	Degradation of carbonyl-containing compounds (esters, acids).	Thanavel et al.27
	1685–1650	Moderate peaks for C = O stretching in amides or conjugated carbonyls.	Reduced or absent.	Degradation of amides or conjugated carbonyls.	Banerjee et al.28
	1619–1516	Strong peaks corresponding to C = C stretching in aromatic rings.	Reduced significantly post-treatment.	Partial breakdown of aromatic compounds (e.g., dyes).	Khan et al.21; Rathour et al.24; Banerjee et al.28; Khan and Joshi,23
	1463–1422	Moderate peaks for C–H bending vibrations.	Slight reduction in intensity.	Minor degradation of aliphatic hydrocarbons.	Rathour et al.24
	1253–1031	Strong peaks for C–O stretching in esters, ethers, or alcohols.	Significantly reduced post-treatment.	Breakdown of oxygen-containing compounds such as esters or ethers.	Meenatchisundaram et al.20; Thampraphaphon et al.26
	897–630	Weak peaks for C–H out-of-plane bending in aromatic rings.	Reduced significantly or absent.	Degradation of aromatic structures.	Banerjee et al.28; Thanavel et al.27; Thampraphaphon et al.26

Fig. 11.

GC-MS chromatograms of effluent samples from various textile industries comparing the pre- and post-treated samples.

Discussion

Textile effluent and soil/sludge are laden with toxic dyes as well as chemicals, harbouring distinct microbial population that has adapted to survive under such harsh conditions over time. This adaptation might be attributed to the activity of microbes to degrade those recalcitrant compounds utilizing them as energy sources, thereby contributing to environmental remediation^29,30. The effluent may exhibit broad range of pH and temperature ranges depending upon varied industrial processes and environmental conditions. Both parameters greatly influence the chemical and biological processes in water³¹. The present study assessed the biodegradation potential of screened bacterial isolate, Pseudomonas stutzeri for remediation of textile effluent at industrial scale. It evaluated the effects of pH, temperature, carbon, and nitrogen sources on the dye degradation efficiency of Pseudomonas stutzeri strain BS106, and providing insights into the optimal conditions for maximizing its bioremediation potential. According to the results, it performs optimally at near neutral conditions (pH 6–7). Another report suggested that the decline in efficiency beyond this range is attributed to enzyme denaturation or altered substrate availability under extreme pH conditions³². Subsequently, indicated by another group that the neutral pH range likely supports optimal enzyme structure and charge, facilitating substrate binding and catalytic activity. Also, it was noted that there is critical role of pH in maintaining enzymatic activity and cell viability. These results align with previously reported studies stating that Pseudomonas aeruginosa, Pseudomonas plecoglossicide MG2, perform the optimal degradation at neutral range of pH^33,34. Contrastingly a novel strain of Pseudomonas sp. YB2 demonstrated 90–100% dye decolorization at broad pH range of 5–10. This high rate of decolorization at increasing alkaline conditions was attributed to chemical transformation of malachite green to malachite green leucocarbinol rather than biodegradation³⁵. The metabolism of bacteria and its enzymes activity are also highly sensitive to thermal conditions. Extremely high temperature leads to the denaturation of enzymes and microbial growth decrease significantly at lower temperature, causing an impact on efficiency of microbes³³.

In the current study, the optimal temperature was found to be 37 °C supported by other studies on Pseudomonas aeruginosa, Streptomyces DJP15 reported optimum temperature to be 45 °C and 35 °C, respectively, which facilitates optimal enzyme activity and cellular processeS^33,36. Similarly, glucose acts as the best co-substrate for the screened bacterial isolate because of its simpler structure and efficient metabolic pathway. It is supported by the studies that revealed Pseudomonas putida SKG-1, Pseudomonas putida and Bacillus licheniformis, Pseudomonas taiwanensis strain TNZ3 and some halotolerant bacterial strains has shown preference for glucose for efficient degradation of dyes^5,37,38. The preference for monosaccharides highlights the importance of selecting optimal carbon sources to maximize microbial efficiency in bioremediation efforts. Conversely, the plateauing effect observed with sucrose and maltose suggests metabolic limitations or substrate inhibition at higher concentrations, consistent with prior studies³⁹. Another potent source that can be used as co-substrates for efficient activity of microbes is nitrogen (inorganic, organic). In the current study, yeast extract exhibited the best activity across all concentrations, followed by proteose peptone. Alternatively, a study suggested that the rich composition of these nitrogen sources helps in bacterial growth and boosts their metabolic rate. The antagonistic effect of ADP on efficiency of bacteria can be attributed to inorganic source of nitrogen. This might indicate that there is lack of readily utilizable organic compounds needed for optimal enzymes induction⁴⁰. Nasrin et al.⁴¹ also reported that combination of yeast and peptone extract act as the best nitrogen source for efficient decolorization of dyes. It was also reported that addition of tryptone in M9 media supplemented with Red 2G dye efficiently degraded 45.47% dye in comparison to tyrosine, peptone, glycine and ammonium ferrous sulfate (5% − 23%)⁴². Several studies investigating the role of nitrogen co-substrates have consistently demonstrate that organic sources are more effective compared to their inorganic counterparts⁴³.

Microbes undergo various metabolic changes when exposed to the stress. The exposure of bacteria to the toxic dyes and chemical in textile effluent leads to the changes in its protein profile, which assist the bacteria to degrade and detoxify the effluent. The metabolic adjustments observed in Pseudomonas stutzeri under effluent stress indicate a well-coordinated cellular response to manage environmental toxicity. The bacterium prioritizes detoxification mechanisms, including oxidative stress management and efflux transport, while reducing energy-intensive processes like protein synthesis, biosynthesis, and motility. The observed upregulation of stress proteins and downregulation of biosynthetic processes reflect a shift toward energy conservation and stress tolerance, enabling Pseudomonas to adapt to effluent-induced environmental stress effectively. Additionally, the increased abundance of chaperones and heat shock proteins highlights the role of protein stability and repair mechanisms in ensuring survival under adverse conditions. The downregulation of biosynthetic pathways, growth-related proteins and motility-associated proteins reflects an energy-conserving strategy, allowing the bacterium to allocate resources to critical stress tolerance mechanisms. These findings provide valuable insights into the adaptive strategies of Pseudomonas stutzeri and its potential applications in effluent treatment and pollutant degradation. Mishra & Verma⁴⁴ analysed the protein expression in Pseudomonas mendocina SMSKVR-3 in response to arsenate stress. Protein that overexpressed to sustain these stressful conditions was identified as ribosome recycling factor, phosphate: ADP/GDP phosphotransferase, ribonuclease P protein component, and cobalt-precorrin-5B-methyltransferase. Other studies reported that the stress protein induction in bacteria revealed that there was increase in efflux and influx transporters, carbon metabolism, envelope function, and macromolecular turnover in response to heavy metal rich textile effluent^45,46. MALDI-TOF and LC-MS/MS analysis of Pseudomonas aeruginosa assessed the effect of chromium stress in cell. They reported overexpressed proteins involved in stress response, proteins responsible for energy production, outer membrane proteins (MucD) free radical detoxification by glutathione system while downregulated proteins were isocitrate dehydrogenase and 30 S ribosomal proteins^47,48. During stress response, bacteria supress the production of some proteins such as ribosomal proteins and nucleotide biosynthesis⁴⁹. Proteins like PurM, PurL and PyrH that are involved in nucleotide biosynthesis are repressed, reflecting reduced DNA and RNA synthesis during stress⁵⁰. Other proteins involved in cell motility (flaellin FliC) and transcription factors are downregulated under effluent stress, prioritizing energy for survival rather than movement^50,51.

The treatment of textile industrial effluent using microbes not only decolorizes the dyes but also degrade/detoxify/mitigate the chemical compounds (organic as well as inorganic) and heavy metals. The UV-Visible spectra of effluent under in vitro and on-site conditions show the bioremediation of effluent using Pseudomonas stutzeri. According to the previously reported studies, the absorbance in UV region of the spectrum can be attributed to the aromatic compounds in the effluent⁵². The organic compounds having double bond, triple bonds, carbonyl groups, peptide bonds, and aromatic systems are denoted by UV region of the spectra (200–400 nm). Specifically, the peaks at 280–300 nm are present because of the suspended solids⁵³. Whereas the light transmitted by the solution is depicted by absorbance at 400–800 nm. Herrera-Ibarra et al.⁵⁴ reported 92.46% and 99.77% degradation at 226 nm and 515 nm, respectively, indicating the degradation of aromatic/double bonds and visible discoloration of the textile effluent. The dynamic and enriched environmental conditions at industrial scale treatment complement the activity of screened bacterial strain, Pseudomonas stutzeri. The indigenous microbes along with screened microbe help in the biodegradation of recalcitrant present in the effluent leading to better percentage degradation in UV range at on-site treatment. There was not much difference observed in color removal between in vitro and on-site scale, as Pseudomonas stutzeri itself bears high dye degradation efficacy. Because of this property, dye degradation at in vitro conditions was as efficient as on-site conditions. The study supported by Shedbalkar & Jadhav⁵⁵ revealed that Penicillium ochrochloron efficiently decolorized 93% dye in the effluent within 14 h. These findings underscore the role of microbe based treatments for industrial effluents for addressing the environmental challenges.

Bacteria can efficiently mitigate heavy metals but the efficiency for specific metal is dependent on the metal’s chemical speciation, its conjugate forms or bioavailability. These factors influence its accessibility to bacterial uptake or enzymatic activity⁵⁶. The P-type ATPase CopA in Escherichia coli relies on exporting Cu⁺ from the cytoplasm to the periplasm, where the multicopper oxidase CueO converts the more hazardous Cu⁺ into the less reactive Cu²⁺. Similarly, mercuric reductases catalyze the reduction of highly toxic Hg²⁺ to volatile Hg⁰, enabling its removal from aqueous environments⁵⁷. The single bacterium, Pseudomonas stutzeri under in vitro conditions and along with native microbial population at on-site treatment, detoxifies the heavy metals from effluent effectively. The studies conducted earlier related to heavy metal mitigation by microbial isolates aligns with the results of current study. Jadhav et al.⁵⁶ conducted a study which proved that bacterial consortium of three different species of Pseudomonas (DAS) isolated from textile industrial effluent entirely removed Pb, Ni and Cr from water whereas Fe was partially mitigated. The study also reported that with the increase in pH of the solution, iron mitigation rate was increasing. Bacterial iron detoxification in textile effluents relies on redox cycling, chelation, and precipitation. Dissimilatory iron-reducing bacteria such as Geobacter and Shewanella enzymatically reduce Fe³⁺ to Fe²⁺, affecting iron mobility and co-contaminant fate. Iron-oxidizing bacteria perform the reverse, precipitating Fe³⁺ as insoluble hydroxides. Siderophores chelate excess Fe³⁺, while extracellular polymeric substances in biofilms bind and aggregate iron, therefore facilitating removal⁵⁸. Hence, it was proved that pH and ionic strength plays important role in the removal of heavy metals from the effluent. Another study by Hussain et al.⁵⁹ constructed a pilot scale vertical flow wetland system augmented with bacterial endophytes. The comparative analysis of wetland versus wetland with bacterial augmentation showed that the results were exceptionally good when bacteria were integrated in the system. The high detoxification of Chromium, iron, nickel and cadmium from the effluent was observed. Previous studies reported efficacy of textile effluent degrading bacteria to inhibit Potentially Toxic Elements (PTEs). They isolated 14 metal reducing bacteria from the wastewater and evaluated their efficacy for metal removal. Out of 14 isolates, six exhibited potential in reducing (61%-99%) cadmium, copper, and chromium²⁹. Collectively, the enzymatic and physicochemical strategies including biosorption, intracellular sequestration, redox transformations, bioleaching, and bio mineralization underscore the potential of bacterial systems as eco-friendly, cost-effective alternatives for heavy metal removal in textile wastewater treatment⁶⁰.

Physiochemical parameters of effluents need to be specifically under standard limit set by regulatory agencies in order to contribute for the prevention of the environmental pollution. These parameters analysed after treatment with Pseudomonas stutzeri at industrial scale, found to be under the limits set by MoEF. These parameters are dependent on each other as decrease in pH also contributes in reducing the TDS as it helps in neutralizing charge between the particles leading to precipitation of compounds. Various studies reported the changes in these parameters at laboratory and pilot scale. Textile wastewater treated with total aerobic mix bacteria significantly reduced the physicochemical parameters of wastewater and bring it under Effluent Quality Standards set under the Environment Conservation Rules (ECR), 1997. The pH of effluent was reduced from 10 to 6.9; COD: 1100 mg/L to 170 mg/L in 48 h; BOD: 600 mg/L to 50 mg/L in 60 h; TDS: 2100 mg/L to 320 mg/L in 60 h; Dissolved Oxygen (DO): 1.8 mg/L to 6.7 mg/L in 72 h. Another study by Tara et al.⁶¹ revealed that Floating Treatment Wetlands (FTWs) along with dye degrading bacteria are highly efficient in treating textile industrial wastewater. It not only reduced physico-chemical parameters but also helped in heavy metal mitigation. Similar study⁶² also stated the potential of Enterobacter sp., Microbacterium arborescens and Pantoea stewartii in reducing BOD, COD, TDS, TSS and Cr by 70, 63, 57, 87, and 54%, respectively in tannery effluent. Another study reported that Pseudomonas aeruginosa and Bacillus subtilis immobilized on agar-agar in a bioreactor showed reduction (%) in COD, BOD, TS, TSS, TDS as 83%, 97%, 95%, 88%, 96% respectively⁶³.

Bacterial enzymatic systems play a diverse and pivotal role in breaking down and detoxifying dyes along with other xenobiotic compounds present in textile wastewater. Among these, oxidoreductive enzymes such as azoreductases initiate the reductive cleavage of azo linkages (–N = N–) in synthetic dyes, producing aromatic amines that can subsequently be metabolized through hydroxylation and ring-opening reactions mediated by monooxygenases and dioxygenases⁶⁴. Enzymes like laccases and lignin peroxidases, commonly reported in Bacillus and Pseudomonas species, oxidize phenolic moieties and aromatic amines, disrupting chromophore structures and achieving substantial decolorization^30,65. Tyrosinases contribute by catalyzing the ortho-hydroxylation of monophenols followed by the oxidation of o-diphenols into quinones, which then polymerize and precipitate, thereby lowering the solubility and mobility of toxic intermediates. Nitroreductases target nitroaromatic dye byproducts, converting them into less toxic amines and improving their biodegradability. Catechol 1,2- and 2,3-dioxygenases further enable aromatic ring cleavage, ultimately leading to mineralization into carbon dioxide and water⁶⁶. Additionally, hydrolases such as esterases and amidases degrade surfactants and auxiliary processing chemicals, supporting the overall detoxification process⁶⁷. The cooperative action of these enzymes within bacterial consortia ensures both color removal and toxicity reduction, underscoring their promise as eco-friendly agents for textile effluent treatment. Notably, Pseudomonas stutzeri isolated in this study effectively degraded complex hydrocarbons, aromatic dyes, and other organic pollutants from textile effluent. The observed reduction in peak intensities across functional groups in Table 5 confirms degradation. The mineralization of dyes and chemicals has confirmed the disappearance of aromatic and azo linkages indicating structural transformations to hydroxyl and carboxyl groups. The studies reported earlier showed that IR peaks detected belong to the hydroxyl, carbonyl, amines and aromatic bonds in functional groups^{19–27,68–70}. Similarly, the effluent samples analysed from various textile hub revealed a mixture of cyclic aliphatic, aromatic organic compounds such as benzoic acids, long chain hydrocarbons phenols, naphthalene derivatives and sulfonates through GC-MS, which aligns with the results observed in the current study^70–75.

The cumulative findings of these studies indicate that bacteria can be used as efficient bioremediation agents for toxic elements in the wastewater released from the industries. While many previous studies reported the use of microbes for remediation of textile industrial effluent, this study is particularly notable as it addresses the treatment of large scale effluents ranging from 50 to 500 MLD (million litres per day). In contrast, earlier research predominantly focussed on laboratory scale and pilot scale treatments, typically limited to a maximum of 1000 L effluent. This bacterially driven approach for treatment of textile industrial effluents helps in addressing a critical environmental challenge. It significantly contributes to the industrially feasible deployment tactic to address various problems associated with the discharge of dye and chemical loaded effluent from industries by improving the overall water quality. The 80–85% of upcycled water can be reused by the industry for various processes. A key highlight of this study is the successful translation of laboratory scale findings into patented process and product, reinforcing its industrial applicability. The granted Indian patent¹⁹ (Patent Grant: 556875) underscores the novelty and effectiveness of the developed technology, hence providing a robust solution for real world industrial application.

Conclusion

The current study emphasizes the potential of the bacterial isolate, Pseudomonas stutzeri in the bioremediation of textile effluents under industrial-scale conditions. Initial optimization of the isolate was conducted under in vitro conditions, followed by validation through industrial-scale trials. High-throughput analyses identified various proteins/enzymes as acting moieties in Pseudomonas stutzeri responsible for the biodegradation of effluent. The findings proposed by the current investigation are first to report based on the literature review from on-site industrial trials, encompassing decolorization, degradation, and detoxification. Future studies should focus on elucidating metabolic pathways of strain BS106 and exploring targeted genetic modifications to enhance its efficacy for degrading broad range of industrial contaminants. In parallel, the authors are currently conducting plant and animal bioassays to evaluate the safety and ecological impact of the treated effluent on surrounding flora and fauna. These ongoing efforts aim to further ensure the environmental compatibility and sustainable application of current bacterial strain in industries.

Author contributions

TB, SG and AKD have designed the research study; TB and SG performed the research; SG and AKD provided guidance conceptualization during the study conduct. TB wrote the first draft of the manuscript, drawn inference using software, managed the analysis of the study through managing the literature searches. All the authors read and approved the final manuscript.

Data availability

The 16 S gene sequence of Pseudomonas stutzeri is available in NCBI database under accession number OM133769.1. The strain, Pseudomonas stutzeri was deposited in National Centre for Microbial Resource (NCMR), National Centre for Cell Science (NCCS), Pune with accession number MCC 4702. All relevant data are within the manuscript and its supporting information files.Ref. source: NCCS (National Centre for Cell Science): https://www.nccs.res.in/. NCMR (National Centre for Microbial Resource): https://ncmr.nccs.res.in.

Declarations

Competing interests

The authors declare no competing interests.