Introduction

The increasing demand for energy and depletion of fossil fuel-based energy sources made compulsion to search for an alternative sustainable energy source1. Natural gas, coal, and oil are examples of fossil fuels that account for 81% of primary energy consumption2. The global primary energy consumption rate of fossil fuels increased from 40,494.04 TWh (terawatt-hours) in 1965 to 140,230.67 TWh in 2023. Due to the high fossil fuel consumption rate, we are left only with 236.3 billion tonnes of oil proved reserves and 1.074 trillion tonnes of coal proved reserves3. Along with these limitations, the combustion of fossil fuel emits greenhouse gases that disrupt the global temperature and atmosphere4,5. As constraints grow, the finding of alternate eco-friendly sustainable energy sources becomes more crucial. As a renewable resource, lignocellulosic biomass can be the best alternative resource that may be used to make biobased products and bioenergy1. The use of lignocellulose biomass will not only overcome energy necessity but also overcome the pollution caused by the burning of agricultural wastes and paper industries.

The most prevalent type of lignocellulosic biomass on the planet is cellulose, which makes up the majority of the biomass found in plants6. About 35–50% of the plant’s dry weight is made up of cellulose, which is tied with hemicelluloses and lignin in lignocellulosic biomass7. Considering that it predominates in the form of stalks, stems, and husks from the agricultural business, there is a lot of curiosity about using cellulose as a feed or a power resource1.

Cellulose is a homo-biopolymer containing repeated D-glucose units linked by β−1-4 glycosidic linkages8. Chemical hydrolysis and enzymatic hydrolysis can both be used to break down cellulose into simple sugar. However, as reported by Mandel in 1974, enzymatic hydrolysis is generally preferred because it forms fewer by-products. The conversion of cellulose to fermentable sugars relies on the cellulase enzymes, which can degrade it into fermentable sugars. On the basis of hydrolytic mechanism, cellulase enzymes are grouped into endoglucanases (E.C. 3.2.1.4), cellobiohydrolase (EC 3.2.1.91) and β-glucosidase (E.C.3.2.1.21)9. Endoglucanases (E.C.3.2.1.4) randomly cut the cellulose polysaccharide chain at internal amorphous regions, producing oligosaccharides of different lengths9. Cellobiohydrolase (E.C.3.2.1.91) works on both the reducing and the non-reducing ends of cellulose polysaccharide chains to release cellobiose units. β-glucosidase (E.C.3.2.1.21) hydrolyses terminal non-reducing β-D-glucose residues to release β-D-glucose10. Cellulases have been widely used in a variety of industries for around 30 years, including the food, animal feed, brewing and winemaking, agriculture, biomass refining, pulp and paper, and textiles industries11.

Cellulase enzyme is generated significantly by bacteria, fungi, and algae. A significant portion of commercial cellulases is produced by various fungi owing to their high enzymatic activity; however, various characteristics indicate that bacteria may have enormous potential12. Fungi have historically been employed in several fermentation sectors. However, the economic viability of utilizing fungi for alcohol production may be hindered by the complicated culture process13. Regardless of their great productivity, fungal fermentations at larger scale are a challenging process due to the broth’s complicated rheology during the biomass growth phase. The hyphal growth leads to increased broth viscosity, which hinders agitation and aeration, consequently reducing mass transfer14. In contrast to their fungal counterpart, bacterial strains are more suitable for bioethanol synthesis due to their rapid growth rate, ability and inherent diversity to flourish in various environmental niches, enabling them to tolerate various forms of physical and chemical stress. Furthermore, bacterial cellulases are frequently more complex and are found in multi-enzyme complexes, resulting in improved function and synergy15. Above all, bacterial enzymes are easier to use for genetic purposes to increase their enzymatic activities and have a greater preference for being heat stable. Isolation of cellulolytic bacteria for industrial applications has been studied extensively from variety of environments, including soil, fecal samples, hot spring water, and leaf litter16. Cellulolytic bacterial species such as Bacillus cereus A4917, Cohnella xylanilytica RU-1418, Bacillus licheniformis Z9, and Klebsiella oxytoca Z6319 have recently been isolated from various agricultural soils. Some other recently isolated cellulolytic bacteria include Lysinibacillus fusiformis strain isolated from White Grub Bettle larvae gut20, Bacillus pumilus XM isolated from the fecal sample of Père David’s deer (Elaphurus davidianus)21, Bacillus sp. YE16 isolated from yak dung22. These strains are employed for biomass degradation, as well as biofuel production using various biomass.

While many cellulolytic bacterial strains are isolated from variety of source that generate substantial amounts of free enzyme but still researchers are trying to identify potential cellulolytic bacterial strain that possess multifunctional activity like CMCase, glucosidase, pectinase, xylanase and β−1,3-glucanase etc. in mesophilic range. Recently, mesophilic enzymes are getting more demand for their use in simultaneous saccharification and fermentation (SSF) to reduce the steps and cost for bioethanol production. SSF process, requires the mesophilic enzymes having optimum temperature around 30˚C-45˚C to degrade the lignocellulosic biomass resulting in the release of monosaccharides which can be further converted into biofuel23. In the textile industry, mesophilic cellulase enzymes can effectively remove loose fibers and fuzz for enhancing the smoothness of fabric without overly degrading the fabric’s inherent tensile strength, whiteness index, and elasticity24. Therefore, in this study, cellulolytic bacteria were isolated from cotton (Gossypium arboretum) and brinjal (Solanum melongena) soil from the Panchmahal region, Gujarat, India. Cotton and brinjal soil are rich sources of organic content and plant residues that may provide favourable conditions for the diversity of mesophilic microorganisms and it has been reported if enzymes isolated from mesophilic bacteria, it will have higher chances to be mesophilic in nature25. The potential cellulolytic bacteria were screened out on the basis of zone of hydrolysis produced by bacteria on CMC-BH agar plate and enzyme activity of bacterial culture supernatant. The selected bacteria were identified as E. faecium and S. stutzeri on the basis of biochemical and 16 s rRNA analysis. It is the first instance of a mesophilic cellulase being reported from an S. stutzeri. The CMCase production was further optimized using one factor at a time (OFAT) and Response surface methodology (RSM) method and followed by crude enzyme biochemical characterization. Crude enzyme of both isolates showed maximum activity in mesophilic temperature range therefore, in this study efficacy of crude enzymes from E. faecium and S. stutzeri in biopolishing of cotton fabrics was assessed.

Methods

Substrate and chemicals

The Bushnell Hass broth, agar powder, gelatin agar, glucose phosphate broth, lead acetate paper strips, Lugol’s iodine, nutrient broth, oxidase discs, Simmons’s citrate agar, starch agar, triple sugar iron agar, tryptone broth, Bradford’s reagent, and 3,5-dinitrosalicylic acid were purchased from Hi-Media Pvt. Ltd., India. Carboxymethylcellulose (CMC), p-Nitrophenyl-β-D-glucopyranoside (pNPG), beechwood xylan, and apple pectin were purchased from Sigma-Aldrich, USA. All chemicals and reagents were of analytical grade.

Isolation of cellulolytic bacteria

One gm of each soil was suspended with 50 mL of (0.85% w/v) sterile NaCl solution in a 250 mL conical flask and incubated for 30 min at 37°C and 180 rpm. These soil samples were serially diluted up to 10−8 in sterile saline solution. Aliquots of 0.1 mL from each dilution were spread onto carboxymethylcellulose amended Bushnell Hass (CMC-BH) agar plates and incubated for 72 h at 37°C. Colonies with different morphologies were selected, and repeated streaking on CMC-BH agar plates was done to purify bacterial colonies26. CMC-BH agar plates containing purified colonies were stored at 4°C for further examination.

Screening of cellulolytic bacteria

Each isolated pure colony was picked up from above incubated plates and inoculated into LB broth for 14 h at 37℃. 3 µL of each freshly grown bacterial culture were dropped on CMC-BH agar plates and further incubated at 37°C for 48 h27. Each incubated CMC-BH plate was flooded with 1% Congo red solution for 15 min. After 15 min, the Congo red solution was discarded and counterstained with 1 M NaCl solution for 10 min28. A clear hydrolytic zone around each colony was observed, and the best cellulolytic bacteria were selected on the basis of hydrolytic zone size. For quantitative analysis, isolates with a significant zone of hydrolysis were grown in LB broth and incubated overnight at 37°C in a shaking incubator at 180 rpm. 0.5 mL of grown culture was inoculated into 50 mL of enzyme production media containing (g/L): carboxymethyl cellulose (CMC) (10), yeast extract (4), and Bushnell Hass broth (3.27) and incubated for 72 h at 37°C and 180 rpm. The incubated cultures were centrifuged at 10,000 g for 10 min to collect supernatant containing crude enzyme for estimation of enzyme activity by 3,5-dinitrosalicylic acid (DNS) method22.

Identification of cellulolytic bacteria

Bacterial isolates were identified on the basis of morphology, Gram staining, and biochemical tests. Morphological studies were done by analysing bacterial size, shape, cell arrangements, and pigmentation. To distinguish the isolates as gram positive and gram negative, the Gram stain technique was used29. Isolates were also identified by performing different biochemical tests like the indole test, triple sugar iron agar test (TSI), methyl red, Voges-Proskaur, oxidase test, catalase test, and nitrate reduction test and compared as described in Bergey’s Manual of Systematic Bacteriology30.

Biochemical tests

Indole production

A loopful of the test culture was added to the tryptone broth and incubated for 24 h at 37℃. After incubation 1 mL of Kovac’s reagent was added to the broth and observed for the formation of a pink ring13.

MR-VP (Methyl Red-Voges proskaur) test

Glucose phosphate broth (GPB) was prepared for the MR-VP reaction containing g/L (peptone 7.0, glucose 5.0 and phosphate buffer 5.0). GPB was inoculated with test culture and incubated at 37℃ for 24–48 h. For MR test 5 drops of methyl red indicator were added after incubation and observed for the development of red colour. For VP test, after incubation, 0.6 mL of α-naphthol and 0.4 mL of KOH solution were added and observed for the red colour development after 15–60 min31.

Oxidase test

The cytochrome oxidase test is helpful in determining the types of bacteria that produce the cytochrome oxidase enzyme. The transfer of electrons from the donor to the ultimate acceptor (oxygen) is the mechanism of cytochrome oxidase. Cytochrome oxidase will oxidize the electron donor, resulting in the creation of indophenol and a dark purple colour. Filter paper was treated with 1% tetra-methyl-p-phenylenediamine dihydrochloride (TMPPD), a synthetic electron donor, and dried. The bacterial colony was picked up using the edge of a sterile cover slip or wooden toothpick for the test. Within ten seconds, a blue-purple colour shift was observed32.

Catalase test

With the help of a sterile nichrome wire loop, an isolated bacterial colony was transferred to the surface of a sterilized, dry glass slide, and a few drops of 3% hydrogen peroxide (H2O2) solution were added to the glass slide and observed for the formation of gas bubbles or effervescence13.

Nitrate reduction

Test culture inoculated into peptone nitrate broth (PNB) and incubated at 37℃ for 24 h. After 24 h of incubation, 0.5 mL of reagent A (sulfanilic acid) was added, followed by the addition of 0.5 mL of reagent B (α-napthylamine) and observed for red colour to confirm the presence of nitrite. In cases where no colour change occurs, the amount of unreduced nitrate in the tube either stays unreduced or can be confirmed by adding zinc powder, a potent reducer that converts nitrate to nitrite and tints the culture red. If there’s no colour change after addition of zinc powder, the sole possible reason is that the nitrate was reduced to nitrite and then further reduced to other nitrogenous compounds32.

Urease test

A loopful of a test culture was inoculated into the urea broth and incubated at 37°C for 24 h. After the incubation, tubes are observed for the development of pink colour31.

Starch hydrolysis

Test culture was streaked on a starch agar plate containing g/L: starch soluble (2), agar (15), peptic digest of animal tissue (5), and meat extract (3) at pH 7.0 and incubated at 37℃ for 24 h. Plates were flooded with Lugol’s iodine and observed for clear, colourless zone around growth due to the starch hydrolysis31.

Triple sugar iron agar test

A loopful of test culture was streaked on TSI agar slant medium containing g/L (peptone 20.0, hydrolysate 6.0, sodium chloride 5.0, glucose 1.0, lactose 10.0, sucrose 10.0, ferrous citrate 0.30, sodium thiosulphate 0.30, phenol red 0.025, and agar 12.0) and stabbed the same on the butt of the slant. The TSI agar slant was further incubated at 37℃ for 24 h and observed for the acid/gas/H2S production in the butt as well as on the slant33.

Molecular identification of bacterial isolates

Yaazh Xenomics in Coimbatore, Tamil Nadu, India, carried out the 16 S rRNA amplification, sequencing, and analyzed for the molecular identification of the bacteria. DNA isolation from bacterial samples was done using the Qiagen DNA extraction kit developed by Qiagen. The resulting 16 S rRNA genes of the new isolates were amplified by thermocycler (Biometra thermocycler, Germany) using the universal primers 27 F (5´-AGAGTTTGATCCTGGCTCAG-3´) and 1492R (5´ GGTTACCTTGTTACGACTT-3´)34. The unknown organism was identified using the maximum aligned sequence through BLAST search, and CLUSTAL W was used for sequence alignment35. Phylogenetic trees constructed using the Maximum Likelihood method using MEGA 12.036.

Enzyme assay

Preparation of crude enzyme

The isolates with the best zone of hydrolysis were grown in LB broth medium and incubated overnight at 37 °C in a shaking incubator at 180 rpm. The cellulase enzyme was produced by inoculating 500 µL of grown culture into 50 mL of enzyme production media containing (g/L) carboxymethyl cellulose (CMC) (10), yeast extract (4), and Bushnell Hass broth (3.27) and incubated at 37°C for 72 h at 180 rpm. Grown cultures were centrifuged at 10,000 g for 10 min to collect supernatant for further estimation of enzyme activity22,37.

Endoβ−1,4glucanase activity assay

The CMCase activity was measured by the 3,5-dinitrosalicylic acid (DNS) method. The reaction mixture comprised 300 µL of crude enzyme and 600 µL of 0.5% carboxymethyl cellulose (CMC) in 50 mM sodium phosphate buffer (pH 7.0) and was incubated at 37°C for 30 min in a water bath. The addition of 900 µL of DNS reagent was used to stop the process. The mixture was then boiled for 10 min followed by cooling at room temperature and the addition of 900 µL of distilled water. At 540 nm, the OD of the samples was evaluated in comparison to a blank that contained all the reagents excluding crude enzyme37. Each experiment was performed in triplicate, and one unit of activity is defined as 1 mL of enzyme solution that catalyses CMC hydrolysis and produces 1 µg of glucose per minute under specific assay conditions17.

A glucose standard curve was prepared using a protocol given by Jain et al.38. 900 µL of samples containing different concentrations (30, 60, 90, 120, 150, 180, and 210 µg/mL) of glucose were prepared in distilled water. 900 µL of DNS reagent was added to each glucose sample and incubated in a boiling water bath for 10 min. After the incubation, each sample was cooled down at room temperature, and 900 µL of distilled water was added. The absorbance of each sample was recorded at 540 nm using a spectrophotometer and a standard linear curve between absorbance and glucose concentration was plotted.

β-glucosidase enzyme assay

β-glucosidase activity of crude enzyme was performed using the p-nitrophenyl-β-D-glucopyranoside (pNPG) method using para-nitrophenol (pNP) as a standard. Each experiment was performed in triplicate. A reaction mixture containing 500 µL of crude enzyme with 1000 µL of 5 mM pNPG in 50 mM phosphate buffer (pH 7.0) was incubated for 20 min at the appropriate temperature. 1.5 mL of Na2CO3 (1 M) was added to the mixture to stop the reaction, and absorbance was measured at 405 nm using a spectrophotometer. The reaction mixture containing all reagents without crude enzyme was used as a blank. One unit of activity is defined as 1 micromole (µmol) of p-nitrophenol (pNP) released per minute from pNPG under specific assay conditions39,40.

Effect of different variables on CMCase production

One factor at a time (OFAT) experiment was used to study the effect of different factors on CMCase production. Different variables like incubation periods (3, 6, 12, 24, 36, 48, 60, and 72 h), temperatures (30, 35, 40, 45, 50, and 55℃), pH (4, 5, 6, 7, 8, and 9), and substrate concentration (0.1,0.5,1.0, and 1.5%) were selected in order to enhance bacterial growth and enzyme activity. CMCase activity of BS5 and CS7 isolates was determined using the 3,5-dinitrosalicylic acid (DNS) method as describe in enzyme assay section and growth curve of both BS5 and CS7 isolates at variable culture conditions was determined by measuring absorbance at 600 nm37,41,42.

Response surface methodology (RSM) for optimization of enzyme production

Based on OFAT experiment results, CMCase production by bacterial isolates BS5 and CS7 further optimization by response surface methodology. RSM is a robust statistical tool that examines the cumulative impact of process variables on enzyme production using an amalgam of statistical and mathematical methods43. ANOVA and factorial methods were used to describe the response. Central composite design (CCD) was used to optimize CMCase production and to study the effects of these four variables. The variables were temperature, pH, inoculum volume, and incubation duration. The variables were evaluated at three levels (−1, 0, and + 1). Table 1 showed the levels and range of these four variables. The dependent variable was the maximum CMCase activity, whereas the independent variables were the various critical parameters44. The CCD calculates the total number of experimental possibilities as 2k + 2k + n0, where k represents the number of independent variables and n0 represents the number of repetitions of the experiment at the center point. This study utilized the CCD model with four variables and three levels, including three replicates at the center point to fit a second order response surface. Each response value was based on the average of three replicate studies. A quadratic model equation was used to approximate the statistical connection between the variable and response (enzyme activity), and 3D charts were plotted to demonstrate the relationships between the four test variables and CMCase activity45. Design Expert software version 13.0 was used for constructing experimental design. All the experiments were carried out in the enzyme production medium, and cell-free cultures were collected from the medium after the incubation to quantify the CMCase activity.

Table 1 Coded levels of factors for independent variables tested in central composite design (CCD).
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Effect of different parameters on CMCase activity

Enzyme reaction mixture was incubated with a range of different temperatures (35–50°C), different pH ranges (4.0–9.0), incubation times (10–60 min), and substrate concentration (0.5-2.0%) using citrate-Phosphate buffer and Tris-HCl buffer and the enzyme activity determined using the 3,5-dinitrosalicylic acid (DNS) method37. Each experiment was conducted in triplicate.

Substrate specificity

Various substrates like carboxymethyl cellulose (CMC), p-nitrophenyl-β-D-glucopyranoside (pNPG), beechwood xylan and amylum, curdlan, and apple pectin were used to determine the substrate specificity of the strains BS5 and CS7. The 900 µL reaction mixtures contained 600 µL of 1% (w/v) substrate dissolved in 50 mM phosphate buffer, pH 7.0, and 300 µL of crude enzyme. The reaction mixtures were incubated at 37℃ for 20 min and 100℃ for 10 min, respectively. The addition of 900 µL of DNS reagent was used to stop the process. The mixture was then boiled for 15 min followed by cooling at room temperature and the addition of 900 µL of distilled water. At 540 nm, the OD of the samples was evaluated in comparison to a blank that contained all the reagents excluding crude enzyme46.

Application of cellulase on biopolishing of cotton fabrics

The cotton fabric sample was prepared in a 1 × 1 cm size as per the methodology provided by Sahin et al.47. 300 µL of crude enzyme from E. faecium BS5 and S. stutzeri CS7 were added to 1200 µL of 1 M sodium phosphate buffer (pH 5.0 at 40 °C for BS5 and pH 7.0 at 45°C for CS7), and incubated for 24 h. As a control for this experiment, the identical conditions without crude enzymes were employed. The cotton fabric was removed from reaction mixture after 24 h incubation using sterile forceps and washed twice in boiling water, followed by drying at room temperature. The reduction in weight of cotton fabric after enzymatic treatment was measured by using the equation below47,48,49.

$$\:Weight\:loss\:\%\:=\frac{{w}_{1}-{w}_{2}}{{w}_{1}}\times\:100$$
(1)

.

where W1 is the weight of cotton tissue before treatment and W2 is the weight of cotton tissue after treatment. The resulting reaction mixture after removing the cotton fabric was also analyzed quantitatively using the DNS method for the estimation of reducing sugars46,49.

Data analysis

The data were analysed using Microsoft Excel, and all graphs were plotted using GraphPad Prism version 8.0. A phylogenetic tree was generated using MEGA 12.0 software. For response surface methodology, Design Expert software version 13.0 was used.

Results

Isolation and screening of soil bacteria

Soil samples were collected from cotton and brinjal soil from the Panchmahal region, and a total amount of 13 bacterial isolates were initially isolated. Among 13 isolates, only 2 bacterial isolates (BS5 & CS7) exhibited the best halo zones around the bacterial colony after performing Congo red staining, as shown in Supplementary Fig. S1. The results revealed that bacterial isolates BS5, CS3, CS5, CS6, and CS7 produced transparent zones, and among them BS5 and CS7 exhibited maximum ratios (D/d) (Table 2). While bacterial isolates that failed to exhibit transparent zones around the bacterial colony were consequently excluded from further utilization. For quantitative analysis, isolates with a significant zone of hydrolysis were grown in CMCase production liquid medium. Out of 13 isolates, BS5 and CS7 showed good initial CMCase activity after 72 h of incubation at 37℃ (Supplementary Table 1). Initial CMCase activity of strains BS5 and CS7 was found to be 8.2290 U/mL and 11.0503 U/mL, respectively. CMCase activity of isolated bacterial strains is in range with the activity of some recently known cellulolytic bacterial isolates e.g., Bacillus cereus A49 (15.63 U/mL)17, Cohnella xylanilytica RU-14 (14.5 U/mL)18, Lysinibacillus fusiformis strain WGI4 (0.93 U/mL)20, Bacillus pumilus XM (10.96 U/mL)21, Bacillus amyloliquefaciens strain elh1 (14.04 U/mL)50, and Novosphingobium sp. Cm1 (7.6 U/mL)51.

Table 2 Size of transparent zone produced by bacterial isolates after stained and counterstained with 1% Congo red solution and 1 M nacl, respectively. D, diameter of transparent zone (mm) produce by bacterial isolates; d, diameter of bacterial colony (mm) produce by bacterial isolates.
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Identification of cellulase producing bacteria

The morphology of the isolates was identified using the Gram staining test. Isolate BS5 was found to be Gram-positive cocci, arranged individually or in pairs as observed under a microscope with an oil immersion lens (Supplementary Fig. S2). It showed negative tests for indole production, starch hydrolysis, and H2S production, whereas it was positive for nitrate production and catalase. Based on these findings, the BS5 bacterial isolate was likely to E. faecium52. Isolate CS7 was found to be rod-shaped, non-spore forming, Gram-negative bacteria (Supplementary Fig. S3). It showed positive results for methyl red, oxidase, and catalase tests and was found to be negative for indole production, Voges-Proskauer, urease, and H2S production. These findings indicated that the strain CS7 was likely to S. stutzeri53,54. The bacterial isolates BS5 and CS7 were confirmed as Enterococcus sp. and Pseudomonas sp., respectively, based on both biochemical and morphological traits when compared to Bergey’s Manual of Systematic Bacteriology. Morphological and biochemical characteristics of both isolates have been summarized in (Table 3).

Table 3 Morphological and biochemical characteristics of potential cellulolytic bacterial isolates.
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Molecular identification of cellulolytic Bacteria

Bacterial isolates (BS5 and CS7) were also subjected to 16S rRNA gene sequencing for molecular based identification of bacteria. BLAST analysis of Isolate BS5 16S rRNA gene showed 99.39% similarity with E. faecium, and CS7 showed 100% similarity with S. stutzeri. These results confirmed that BS5 and CS7 bacterial isolates belong to the genera Enterococcus and Pseudomonas, respectively. The outcomes of the 16S rRNA alignment are shown in (Supplementary Table 2). The 16S rRNA nucleotide sequences of BS5 and CS7 were submitted to the NCBI’s GenBank database with the accession numbers OR354911 and OR915422, respectively. A phylogenetic tree of both isolates was generated using gene sequences in MEGA 12.0 by the likelihood method (Fig. 1).

Fig. 1
figure 1

A phylogenetic dendrogram was constructed from a comparative analysis of 16s rRNA gene sequences of bacterial strains BS5 and CS7 with reference sequences from GenBank database. The dendrogram was made using the maximum-likelihood method with bootstrap values (> 60%) using MEGA 12.0 software. The scale bar indicates the number of changes per nucleotide position.

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Glucose standard curve

The glucose standard curve was constructed using a linear regression equation: y = 0.0023x − 0.0123, with an R2 of 0.9966. The quantity of glucose generated after enzymatic activity was calculated using this equation.

Effect of different conditions on CMCase production

In order to maximize bacterial growth and enzyme production, it is essential to optimize a number of factors, including temperature, pH, incubation duration, and substrate concentrations. In this study, several variables were optimized under shaking flask conditions employing a one factor at a time approach. Bacterial isolate BS5 exhibits the maximum bacterial growth and high enzyme production at 40°C and pH 5.0 after 60 h of incubation (Fig. 2). The CS7 isolate, on the other hand, exhibited the maximum bacterial growth and high CMCase production at 45°C and pH 7.0 after 48 h of incubation (Fig. 2). Substrate supplementation in the medium increased both the enzyme activity as well as the bacterial population, Enzyme activity and bacterial growth reached its peak at 1.5% CMC concentration for both bacterial isolates.

Fig. 2
figure 2

Bacterial growth and CMCase production by E. faecium BS5 and S. stutzeri CS7 (a) at different incubation periods (h), (b) at different temperature (°C), (c) at different pH, and (d) at different substrate concentration (%). One unit of activity is defined as 1 mL of enzyme solution that catalyses CMC hydrolysis and produces 1 µg of glucose per minute under specific assay conditions. Each experiment was performed in triplicate, and error bars are presented where applicable.

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Optimization of CMCase production by response surface methodology (RSM)

The CMCase production medium was further optimized by analyzing the four key factors using RSM-CCD. The multiple regression analysis was performed to determine the relationship between the independent variables (temperature (35–50°C), pH (5–9), incubation time (12–72 h) and inoculum volume (0.5–2.0 mL) and dependent variable (CMCase activity) (Table 4). The CMCase activity varied from 6.88 to 20.10 U/mL in BS5 model and from 7.85 to 23.99 U/mL in CS7 model, indicating the dominant impact of the selected variables and their concentrations on CMCase production by E. faecium BS5 and S. stutzeri CS7. As shown in Table 4, the low variation between the predicted and observed values indicated the accuracy of the experiments and the reliability of both models. Based on the model summary and the response values, a quadratic multinomial final regression equation in terms of actual variables were developed using Design Expert version 13.0, which is presented below.

Table 4 A central composite design (CCD) matrix was obtained through stateease software for the optimization of four independent variables (temperature (°C), pH, incubation time (hr), and inocula volume (mL)) to enhance the CMCase production by E. faecium BS5 and S. stutzeri CS7.
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Regression equation for E. faecium BS5:

$$\begin {aligned}\:\text{E}\text{n}\text{z}\text{y}\text{m}\text{e}\:\text{a}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}\:\left(\text{U}/\text{m}\text{L}\right)&=+19.93-1.42\text{A}-0.7249\text{B}+1.50\text{C}+0.2999\text{D}-0.8106\text{A}\text{B}-1.55\text{A}\text{C}+0.4097\text{A}\text{D}\\&\quad+0.3067\text{B}\text{C}+0.1363\text{B}\text{D}+0.6135\text{C}\text{D}-3.50{\text{A}}^{2}-0.4903{\text{B}}^{2}-1.68{\text{C}}^{2}-1.62{\text{D}}^{2}\end {aligned}$$
(2)

.

Regression equation for S. stutzeri CS7:

$$\begin {aligned}\:\text{E}\text{n}\text{z}\text{y}\text{m}\text{e}\:\text{a}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}\:\left(\text{U}/\text{m}\text{L}\right)&=+21.08-0.1704\text{A}-0.7270\text{B}+1.52\text{C}+0.9178\text{D}+0.4912\text{A}\text{B}-0.2438\text{A}\text{C}-0.1853\text{A}\text{D}\\&\quad+0.3153\text{B}\text{C}-0.0852\text{B}\text{D}+0.5453\text{C}\text{D}-0.6258{\text{A}}^{2}-1.63{\text{B}}^{2}-3.37{\text{C}}^{2}+1.13{\text{D}}^{2}\end {aligned}$$
(3)

.

Where A = Temperature (℃), B = pH, C = Incubation time (hr), D = inocula volume (mL).

The analysis of variance (ANOVA) results used to develop the quadratic regression model for CMCase production by E. faecium BS5 and S. stutzeri CS7 and summarized in Table 5. The determination coefficient (R2) obtained from the fit statistics indicates R2 value for BS5 and CS7 were 0.9483 and 0.9619, respectively, indicating that 94.83% and 96.19% of the experimental data are well matched. The predicted R² value for BS5 (0.7665) closely aligns with its adjusted R² value of 0.9001. Likewise, for CS7, the predicted R² value (0.8704) is corresponds well with its adjusted R² value of 0.9263. There was a strong association between the predicted and experimental values when the difference was smaller than 0.2. The constructed model for BS5 and CS7 exhibited an F value of 58.83 and 17.11, respectively, indicating that the models were significant, and the probability that these F-values occurred due to random chance or noise was very low. These results confirmed that the models were valid and were not influenced by random variation. The coefficients were selected based on their corresponding t and p values (Table 5). The overall p value for both models were less than 0.0001, with an F value of 58.83 for BS5 and 17.11 for CS7. Coefficients with high F value and lower p value were considered statistically significant. The regression analysis of the BS5 model revealed that variables, A, B, C, AB, AC, CD, A², C², and D² exhibited highly significant effects (p < 0.01), AD showed significant effect (P < 0.05), whereas D, BC, BD, and B² did not showed any significant effects (p > 0.05) indicating weaker interaction. In contrast, for the CS7 model, B, C, D, and C² were highly significant effects (p < 0.01), CD and B² were significant effects (p < 0.05), whereas A, AB, AC, AD, BC, BD, A², and D² did not showed any significant effects (p > 0.05). The selected quadratic models are well suited for optimization as evidenced by the correlation plots (Fig. 3). The correlation plots indicated that clustering of data points near the sloping line, exhibiting a strong correlation between experimental and predicated values. In this experiment, coefficient of variation (CV) of the constructed model for BS5 and CS7 showed the low values of 9.50 and 11.01, respectively. A lower CV value implies more precision and reliability of the experimental data, while a higher value of CV is usually associated with lower reliability of the experimental data. Three-dimensional (3D) response surface plots of various independent variables impacting CMCase production were generated using the regression Eqs. 2 and 3 (Figs. 4 and 5). The response plots were used to investigate the interaction effects of two variables for CMCase production while holding other variables constant. The contour plots in Figs. 4A, B, and C depicted the interaction of temperature with other variables. In Fig. 4A, the contour slightly inclined towards the temperature indicating medium pH has the steeper effect on CMCase activity. While in Fig. 4B and C, contour lines showed an inclination towards incubation time and inocula volume, respectively, which means temperature has an influence on both incubation time and inocula volume. In Fig. 4D, the contour was moderately elliptical and slightly elongated along the pH axis. The interaction was significant, and both factors exerted effects on CMCase activity. Figure 4E illustrated the interaction of pH with inoculum volume, where the contour was inclined towards inoculum volume, showing that pH influenced the optimal inoculum volume. Figure 4F depicted the effect of varying inoculum volume and incubation time on CMCase activity, indicating that changes in incubation time had a slight influence on optimal inocula volume. Likewise, Fig. 5A–F depicts the 3D response plots involving various variables affecting CMCase production by S. stutzeri CS7. Most contour plots were elliptical or circular, indicating varying levels of interaction between the variables chosen for CMCase production.

Table 5 Analysis of variance (ANOVA) for the quadratic model obtained from four variables as a result of the central composite design (CCD) for production of cellulase from E. faecium BS5 and S. stutzeri CS7. A, temperature (℃); B, pH; C, incubation time (hr); D, inocula volume (%); C.V.%, coefficient of variation; p > 0.05, not significant; p < 0.05, significant; significant variables were highlighted with italics.
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Fig. 3
figure 3

Graphical representation of the observed enzyme activity (U/mL) of cellulase enzyme against the predicted enzyme activity (U/mL) of cellulase enzyme under the four experimental variables: (a) Predicted vs. Actual cellulolytic activity (U/mL) of E. faecium BS5; (b) Predicted vs. Actual cellulolytic activity (U/mL) of S. stutzeri CS7.

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Fig. 4
figure 4

Three-dimensional (3D) response surface plot showing the influence of independent variables (temperature, pH, incubation period, and inocula volume) on cellulase production of E. faecium BS5. (a) Combined effect of temperature (℃) and pH (b) Combined effect of temperature (℃) and incubation time (hr) (c) Combined effect of temperature (℃) and inocula volume (mL) (d) Combined effect of pH and incubation time (hr) (e) Combined effect of pH and inocula volume (mL) (f) Combined effect of incubation time (hr) and inocula volume (mL).

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Fig. 5
figure 5

Three-dimensional (3D) response surface plot showing the influence of independent variables (temperature, pH, incubation period, and inocula volume) on cellulase production of S. stutzeri CS7. (a) Combined effect of temperature (℃) and pH (b) Combined effect of temperature (℃) and incubation time (hr) (c) Combined effect of temperature (℃) and inocula volume (mL) (d) Combined effect of pH and incubation time (hr) (e) Combined effect of pH and inocula volume (mL) (f) Combined effect of incubation time (hr) and inocula volume (mL).

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Based on above results, CCD analysis provides solutions to optimize the CMCase production parameters. Optimal enzyme production conditions for E. faecium BS5 based on these four independent variables were temperature (41.13℃), pH (5.0), incubation time (55.83 h), and inocula volume (1.33 mL) with predicted enzymatic activity of (20.50 U/mL), and for S. stutzeri CS7: temperature (43), pH (7), incubation time (42 h), and inocula volume (2 mL) with the predicted enzymatic activity of (24.17 U/mL). To verify the reliability of the predicted model by CCD, the predicted conditions were adopted for practical experimentation. The predicted conditions were modified to temperature (41℃), pH (5.0), incubation time (56 h), inocula volume (1.25 mL) for BS5, while conditions for isolate CS7 remained as it is. The maximum CMCase activity for E. faecium BS5 under optimized conditions was found to be 20.04 U/mL against a predicted value of 20.50 U/mL. While optimum CMCase activity by S. stutzeri CS7 was found to be 24.08 U/mL against the predicated value of 24.17 U/mL by RSM-CCD.

β -glucosidase assay

Cellulolytic bacteria E. faecium BS5 and S. stutzeri CS7 produced 5.49 U/mL and 6.17 U/mL of β-glucosidase, respectively, under optimum production conditions. These findings are consistent with β-glucosidase activities of some newly found cellulolytic bacterial isolates. Some other β-glucosidase producing strains include Jiangella alba DSM 45,237 (1.03 U/mL), Lactobacillus rhamnosus L08 (2.74 U/mL), Lactobacillus pantheries (2.19 U/mL), and Bacillus anthracis (2.09 U/mL)55,56,57. One of the primary applications of β-glucosidase has been reported to be the synthesis of biofuel from lignocellulosic waste39. In addition to having significant CMCase and good β-glucosidase activity, the bacterial strains isolated from this study can be used to produce large quantities of the enzyme for industrial use and could facilitate the process of producing bioethanol from lignocellulosic biomass.

Effect of different parameters on cellulase activity

Crude extracts of BS5 & CS7 showed maximum CMCase activity at 20 min incubation time (Supplementary Fig. 4a). The CMCase activity of crude extracts of both isolates was also characterized for its optimum pH and temperature. Crude extracts of isolates BS5 and CS7 showed maximum enzyme activity at 40℃ and 45℃ respectively (Supplementary Fig. 4b). Crude extract of both isolates were also optimized for their maximum enzyme activity at different pH levels. The optimum pH for crude extracts of BS5 and CS7 were 5.0 and 7.0, respectively (Supplementary Fig. 4c). When the pH was raised to the optimal level, enzyme activity increased steadily, followed by a progressive decrease in activity. BS5 and CS7 bacterial isolates exhibited maximum enzymatic activity when the level of CMC concentration reached 1.0%, as shown in (Supplementary Fig. 4 d).

Substrate specificity

According to substrate specificity, crude extract of isolates BS5 and CS7 exhibited remarkably high cellulase activity on carboxymethyl cellulose (CMC) (Fig. 6). This finding suggests a strong ability to hydrolyse amorphous cellulose, which indicates it to be an endo-β−1,4-glucosidase. Crude extract of both isolates also showed activity on pNPG, but it’s around 2-fold lower than CMCase activity. pNPG, which serves as a model substrate for detecting β-glucosidase activity, indicating the presence of enzymes capable of cleaving glucose moieties. A common approach to measure xylanase activity, which is crucial for bacteria that can break down complex plant substances, is to utilize beechwood xylan, a representative source of xylan. Beechwood xylan exhibited moderate activity for both isolates, suggesting a certain degree of hemicellulose activity. Starch, apple pectin, and curdlan a non-cellulosic substrate showed minimal enzyme activity for both isolates.

Fig. 6
figure 6

Substrate specificity of the enzyme of E. faecium BS5 and S. stutzeri CS7 on various substrates was examined. The reaction mixtures containing enzyme and various substrates like CMC, pNPG, beechwood xylan, apple pectin, and amylum (in 1% substrate in 50 mM phosphate buffer, pH 7.0) were incubated at 37℃ for 20 min.

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Biopolishing of cotton fabric

The effect of crude enzyme from E. faecium BS5 and S. stutzeri CS7 on the weight of cotton fabric was studied (Table 6). Cotton fabric treated with crude enzymes from BS5 and CS7 caused a loss in weight of 2.20% and 2.06%, respectively. In contrast, cotton fabric treated with buffer alone lost 0.56% of its weight in the same time frame. Reaction mixtures of cotton fabrics treated with BS5 and CS7 produced 1.67 µmol/mL and 1.62 µmol/mL of reducing sugar after 24 h of incubation respectively, while in the control there was no production of reducing sugars.

Table 6 Percentage weight loss and release of reducing sugar from cotton fabric treated with the cellulase enzyme from E. faecium BS5 and S. stutzeri CS7.
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Discussion

Isolation & identification of cellulolytic bacteria

Numerous cellulolytic enzymes have been identified and isolated from various bacterial strains, because cellulases are used in the textile, pulp, and paper industries, as well as in the fermentation of alcohol and the extraction of fruit and vegetable juices11. It has been demonstrated that bacterial cellulases are superior than their fungal counterparts in terms of endurance to high temperatures, salt, and pH58. Temperature significantly impacts enzyme synthesis by influencing growth rate, dissolved oxygen tension, and the final product formation59. It has been demonstrated that the dynamics of fungus development are significantly influenced by temperature. Molds’ spores and vegetative cells become dormant after 5 min of exposure to 60°C60. Aspergillus and Trichoderma are two of the most widely recognised cellulase producing fungi. Aspergillus niger DWA8 produced the highest level of cellulase at pH 4.5 and 30 °C. When the temperature and pH level rose to 35°C and 7.0, respectively, cellulase activity decreased significantly61. Saravanan et al. isolated Trichoderma reesei, which displayed maximum cellulase activity of 9.23 U/mL at pH 5.5, and a temperature of 37.5°C, yet productivity dropped significantly when the temperature was raised to 40–45°C62. Another important factor that affects the enzyme production during fermentation of lignocellulosic biomass is agitation speed. Agitation speed affects fungal morphology significantly, as increasing speeds cause destruction to fungal hyphae and lower speeds result in pellet formation. Furthermore, mycelial development increases the viscosity of the culturing broth, which may limit nutrient availability from inadequate mixing63. Chung et al.64 studied how agitation speed affects enzyme production and morphological changes in A. niger HFD5A-1 in submerged fermentation. They discovered that higher shear stress, along with increased agitation speed, might result in reduced biomass yields and pectinase synthesis by A. niger HFD5A1. Mustafa et al.60 found that Fusarium begonia, Mucor spp., and Cladosporium spp. thrived best at 70 and 100 rpm. Lower biomass was found as agitation speeds increased, showing that the fungal culture was exposed to stress. It is noteworthy that Bacillus cereus A49 exhibited the highest CMCase activity at 150 rpm agitation speed, with activity being less influenced at 200 rpm. The cellulolytic activities of the bacterial isolates depend on the quantities and sources of biowaste in natural environments65. In this study, soil from cotton and brinjal field were selected as a source for obtaining potential cellulolytic bacteria. Isolate BS5 and CS7 confirmed as potential cellulolytic bacteria by qualitative assay such as Congo-red staining and cellulolytic activity of each isolate was determined by quantitative assay through measuring the amount of reducing sugar by DNS method46,66. Based on morphological, biochemical and molecular characteristics, the bacterial isolates BS5 and CS7 were identified as E. faecium and S. stutzeri respectively. Enzyme production, and CMCase activity by these bacterial isolate strains were tested with different culture conditions like incubation period, temperature, and pH67.

Effect of incubation temperature and pH on bacterial growth and CMCase production

Bacterial growth and cellulase production of both E. faecium and S. stutzeri were significantly influenced by the temperature. In this study, E. faecium and S. stutzeri exhibited the highest cellulase production at 40℃ and 45°C, respectively. The same optimum temperature for bacterial growth and cellulase production for various cellulolytic bacteria has been reported by many studies. Bacillus altitudinis JYY-02 isolated from the soil40 and Streptomyces thermodiastaticus TS4 isolated from the Egyptian soil68 produced maximum CMCase at 45°C. Paenibacillus sp. isolated from the sugar industry waste soil (molasses)69, Bacillus pseudomycoides isolated from the sugarcane bagasse70, and Pseudomonas sp. NE isolated from the fecal sample of Nilgai (Boselaphus Tragocamelus)71 exhibited high potentiality for maximum cellulase at 40°C. This optimal temperature for cellulase production and bacterial growth can be attributed to their environmental conditions and ecological niches. Many cellulolytic bacterial strains isolated from agriculture soil of similar environment showed optimum temperature between 40 and 45°C. Additionally, genetic factors influencing enzyme stability, thermal tolerance, and the bacteria’s ability to adapt to temperature variations likely contribute to these differences in optimal temperature for cellulase production72. Since both isolates in this study were obtained from similar environments, they exhibited comparable optimal temperature ranges.

The existing research indicated that bacterial cellulase may be produced in a wide pH range, between 4.0 and 7.069. The strain and culture medium have a major role in determining the ideal pH for growth and cellulase synthesis. Bacterial isolate CS7(S. stutzeri) exhibited highest cellulase production at pH 7.0, while isolate BS5 (E. faecium) exhibited highest cellulase at pH 5.0. Bacillus pseudomycoides70 and Pseudomonas sp. NE71 exhibited optimum enzyme production at pH 7.0. Biswas et al.13 isolated Bacillus spp. T2-D2 and Pseudomonas spp. E1-PT from the sundarbans mangrove forest soil that exhibited highest CMCase production at pH 7.0. While bacterial strains like Streptomyces thermodiastaticus TS4 form Egyptian soil68 and Raoultella terrigena B13-273 isolated from the humus soil exhibited maximum CMCase at acidic pH 5.0 The varying pH levels of different soils influence the populations of microbes that exist there. Agricultural soils frequently have pH values between 5.0 and 8.0 and bacterial diversity has been demonstrated to peak at neutral pH and progressively decline below and above neutral soil pH74. Multiple lines of evidence imply that the activity and structure of numerous membrane proteins are influenced by ambient pH, which has direct consequences on bacterial metabolic rates75. From a biotechnological perspective, industrial processes are better suited for cellulolytic organisms that function ideally at pH 5.0 to 8.0. Numerous industrial applications, including the production of biofuel and bioremediation, are compatible with this pH range. Furthermore, in industrial circumstances the enzymes produced through these organisms grown to this pH range are more likely to preserve stability and efficiency, enhancing the process’s total yield and efficiency.

Optimization of CMCase production through RSM

In this study, RSM-CCD was employed to optimize four key parameters (temperature, pH, incubation time, and inocula volume) to enhance CMCase production by cellulolytic bacterial isolates BS5 and CS7. A total of 30 experiments with four variables at three levels (−1, 0, and + 1), incorporating various combinations of these variables along with six center-point replicates, were performed, resulting in significantly enhanced CMCase yield and activity. The ANOVA results provide a comprehensive understanding of the critical factors influencing CMCase production by E. faecium BS5 and S. stutzeri CS7, emphasizing the effectiveness of the RSM-CCD based optimization approach. The statistical analysis revealed an F-ratio of 0.9271 (p = 0.5723, p > 0.05) for E. faecium BS5 and an F-ratio of 4.12 (p = 0.0657, p > 0.05) for S. stutzeri CS7, which confirms that the models used for optimization are relevant and suitable. The lack of fit for BS5 was not statistically significant, with an F-ratio of 0.9271 and a p value of 0.5723 (p > 0.05). Similarly, for CS7, the lack of fit was not significant, with an F-ratio of 4.12 and a p value of 0.0657 (p > 0.05). A non-significant lack of fit is desirable for the constructed model, as it suggests that the model is insignificant to pure error and model fits the test data well. Adequate precision quantifies the signal-to-noise ratio, with a ratio greater than 4 being optimal. The ratios for BS5 and CS7 were 13.47 and 16.52, respectively, indicating a strong and reliable signal. Both models showed a non-significant lack of fit (p > 0.05) and high adequate precision, confirming their validity and their utility for industrial optimization. The maximum CMCase productivity was archived by E. faecium BS5 under conditions of 41℃, pH 5.0, 56 h of incubation, and 1.25 mL of inocula volume, while bacterial isolate CS7 achieved maximum CMCase production at 43℃, pH 7.0, 42 h of incubation, and inocula volume of 2.0 mL. Similarly, Wang et al. employed RSM for optimization of cellulase production from B. cereus A49 and concluded that temperature of 36°C, substrate concentration of 2.2%, inocula volume of 4.9 mL were the optimized condition for the cellulase production17. This method has also been used successfully in other studies, such as optimizing CMCase production from Cohnella xylanilytica RU-1418 and the Bacillus amyloliquefaciens strain50. Bacterial isolate BS5 (E. faecium) and CS7 (S. stutzeri) showed maximum CMCase production of 20.04 U/mL and 21.60 U/mL respectively after the optimization through CCD, which was closely aligning with the predicted value of 20.50 U/mL and 24.17 U/mL respectively. After optimization through RSM-CCD, CMCase productivity of E. faecium BS5 increased 2.43 times, reaching 20.4 U/mL compared to 8.22 U/mL under unoptimized condition while CMCase productivity of S. stutzeri CS7 increased 2.18 times, reaching 24.17 U/mL compared to initial unoptimized activity of 11.05 U/mL. The increased in CMCase production after statistical optimization for E. faecium BS5 and S. stutzeri CS7 are comparable with many recently isolated cellulolytic bacterial strains. For example, CMCase activity of Cohnella xylanilytica RU-14 increased nearly 3-fold (from 5.2 U/mL to 14.5 U/mL) after optimization through RSM-CCD18; Bacillus amyloliquefaciens strain elh obtained maximum CMCase activity of 14.04 U/mL after optimization through RSM, which is 2.78 times more than initial unoptimized activity50; Bacillus sp. YE16 produced maximum CMCase of 34.70 U/mL after optimization through RSM, which is up to 2.5-fold from 13.88 U/mL archived through OFAT76; and Raoultella terrigena strain B13-2 isolated from humus soil produced 13.5 U/mL of CMCase after optimization increase of 2.79-fold from unoptimized conditions (4.83 U/mL)73. The CMCase production of BS5 and CS7 is even higher than some of the recently isolated cellulase-producing fungi, such as Aspergillus niger A32 (13.79 U/mL)77, Penicillium oxalicum SM03 (9.1 U/mL)78, and Aspergillus niger ITV02 (11.76 U/mL)79. The results of these investigations suggest that E. faecium BS5 and S. stutzeri CS7 rank among the highest cellulase producers, giving them potential candidates for usage in various industries that require efficient cellulase enzymes.

Cellulases have been widely used in a variety of industries, including biofuel, pulp and paper, and textile industries. Cellulases are essential biofuel enzymes because they can hydrolyse cellulose into glucose. Simultaneous saccharification and fermentation (SSF) is capable of producing ethanol from lignocellulose through the combination of enzymatic cellulose breakdown to produce glucose with the glucose fermentation by yeast such as Saccharomyces cerevisiae. The main disadvantage of employing SSF is the necessity to establish optimal conditions for both enzymatic hydrolysis and fermentation. S. cerevisiae thrives best at pH 4.0–6.0 and temperatures ranging from 35 to 45°C80. As a result, it is critical to find cellulases that perform well under these conditions. BS5 (E. faecium) cellulase thrives at pH 5.0 and 40°C, while CS7 (S. stutzeri) thrives at pH 7.0 and 45°C. These settings are consistent with the ideal growth conditions for S. cerevisiae. As a result, both isolates from this study might serve as valuable sources of cellulase for bioethanol production through SSF. Also, ensuring that enzymes are generated under ideal circumstances could streamline the procedure by minimizing the need for extra stabilizers or modifications. This can result in cost savings and improved manufacturing procedures.

The biostone washing and biopolishing in the textile industry sector favour acidic cellulase enzymes. In biostone washing, acidic endoglucanase enzyme improves abrasion and reduces backstaining when compared to neutral endoglucanase81. Biopolishing process uses cellulase enzymes for smoothening of fabric surfaces. Cellulase can quickly remove fuzz from fabric surfaces, creating a cleaner and smoother appearance and boosting the colour brightness. Acidic cellulase showed a significant role in fiber softness, provided a cleaner surface structure with less fuzz, and was more cost-effective than chemical treatment82. Cellulase enzyme produced in these studies by E. faecium showed optimum pH values of 5.0 which make it useful enzyme for this application.

Cellulases are advantageous for deinking various paper wastes whether employed alone or in conjunction with xylanases. Deinking using cellulase enzymes at acidic to neutral pH eliminates alkaline yellowing, streamlines the deinking process, alters the ink particle size distribution, and lowers environmental pollution82. Cellulase produced by E. faecium and S. stutzeri have optimum pH values of 5.0 and 7.0 respectively, making them valuable in the pulp and paper industry.

Biopolishing of cotton fabrics using cellulase enzyme from E. faecium BS5 and S. stutzeri CS7

Biopolishing is one of the major steps in textile industries for eliminating fibrils from the surface of cotton fabric, because fibrils cause pilling, which reduces the fabric’s smoothness. Some chemical compounds (e.g., acetic acid (CH3COOH), hydrogen peroxide (H2O2), and sodium hypochlorite (NaCIO)) and enzymes break down these fibrils and lower the fabric’s overall weight83. The use of chemicals for this application is limited because chemicals act on both fibrils and on the main body of fabrics, which reduces strength and durability84, as well as causing color fading in cotton fabrics85. In comparison to chemicals, enzymes are preferable because they specifically target the protruding fibrils without damaging the main fabric structure. The weight loss of cotton fabrics after treatment with the crude enzymes from S. stutzeri CS7 and E. faecium BS5 was 2.20% and 2.06%, respectively. This result was comparable to the weight loss by cellulase enzymes from Caulobacter crescentus (2.43%)49, and Thermomonospora sp. (2.0%)86, while it is better than the cellulase enzyme from Streptomyces drozdowiczii that exhibited a weight loss of 0.75% only87. The removal of fibrils was further confirmed by measuring reducing sugar present in the reaction mixture after the removal of cotton fabrics. The results obtained in the biopolishing process of the cotton fabrics indicated that the crude enzymes from BS5 and CS7 can access small superficial cellulose fibrils on cotton fabrics and break them down into small sugar chains, which makes enzymes suitable for textile industries.

Conclusion

Cotton and brinjal soil analysis resulted in the screening of two cellulolytic bacterial strains, BS5 and CS7, which were identified as E. faecium and S. stutzeri, respectively, after the bacterial characterization. Furthermore, the CMCase production by both bacterial isolates BS5 and CS7 were optimized by using RSM-CCD which resulted in highest CMCase activity of 20.4 U/mL for E. faecium and 24.08 U/mL for S. stutzeri. Following the CMCase production, crude extract of both bacteria were analyzed to determine its optimum pH and temperature. Crude extract of E. faecium exhibited maximum activity at pH 5.0 and 40°C while that of S. stutzeri showed at pH 7.0 and 45°C. In addition, the crude enzyme of both isolates demonstrated promising results as a biopolishing agent for removal of cellulosic fuzz remnants from the treated fabric, indicating its usability in textile industries. The crude extract of both isolates showed maximum CMCase activity at mesophilic temperatures, along with multi-substrate specificity making them suitable for variety of industrial applications like bioethanol production, biopolishing and biostoning in the textile industry, and deinking in the paper and pulp industry.