Enhancement of feather degrading keratinase of Streptomyces swerraensis KN23, applying mutagenesis and statistical optimization to improve keratinase activity

Abd El-Aziz, Nagwa M.; Khalil, Bigad E.; Ibrahim, Hayam Fouad

doi:10.1186/s12866-023-02867-0

Enhancement of feather degrading keratinase of Streptomyces swerraensis KN23, applying mutagenesis and statistical optimization to improve keratinase activity

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Published: 30 May 2023

Volume 23, article number 158, (2023)
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Enhancement of feather degrading keratinase of Streptomyces swerraensis KN23, applying mutagenesis and statistical optimization to improve keratinase activity

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Nagwa M. Abd El-Aziz¹,
Bigad E. Khalil¹ &
Hayam Fouad Ibrahim²

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Abstract

In this study, 25 actinomyces isolates were obtained from 10 different poultry farms and tested for their keratinase activity. The isolate with the highest keratinase activity was identified through molecular identification by PCR and sequencing of the 16S rRNA gene to be Streptomyces spp. and was named Streptomyces werraensis KN23 with an accession number of OK086273 in the NCBI database. Sequential mutagenesis was then applied to this strain using UV, H2O2, and SA, resulting in several mutants. The best keratinolytic efficiency mutant was designated as SA-27 and exhibited a keratinase activity of 106.92 U/ml. To optimize the keratinase expression of mutant SA-27, the Response Surface Methodology was applied using different parameters such as incubation time, pH, carbon, and nitrogen sources. The optimized culture conditions resulted in a maximum keratinase specific activity of 129.60 U/ml. The genetic diversity of Streptomyces werraensis KN23 wild type compared with five mutants was studied using Inter-simple sequence repeat (ISSR). The highest total and polymorphic unique bands were revealed in the S. werraensis KN23 and SA-18 mutant, with 51 and 41 bands, respectively. The dendrogram based on combined molecular data grouped the Streptomyces werraensis and mutants into two clusters. Cluster I included SA-31 only, while cluster II contained two sub-clusters. Sub-cluster one included SA-27, and sub-cluster two included SA-26. The sub-cluster two divided into two sub-sub clusters. Sub-sub cluster one included SA-18, while sub-sub cluster two included one group (SA-14 and S. werraensis KN23).

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Introduction

One of the most pressing environmental pollution problems is the accumulation of keratinous wastes in slaughterhouses. Feathers, which are produced from poultry processing industries, are a significant by-product, as they make up 5–7% of chicken body weight. It is estimated that worldwide consumption of chicken generates several million tons of feathers annually, with approximately 2 million tons being generated as a by-product of the poultry industry globally [1, 2].

Despite the many applications of feathers, such as decorative applications, medical devices, fertilizer, dusters, bedding materials, and feedstock, a significant amount of feathers are still released into the environment without proper treatment. Traditional techniques of processing feathers, such as chemical processing and stem pressure cooking, may turn feathers into animal feeds, but they consume a large amount of energy and destroy some amino acids. Additionally, feathers keratin has become a source of pollutants due to its recalcitrant nature [3]. To address this issue, many researchers are interested in finding economic methods to convert feathers into value-added products [4]. Numerous studies have shown that different microorganisms can efficiently degrade feathers [5, 6].

Keratinase is an enzyme that can hydrolyze keratin. Diverse groups of microorganisms are known to produce keratinase [7], including bacteria such as Bacillus subtilis [8] and Chryseo bacterium [9], as well as actinomycetes from the Streptomyces genus [10]. Additionally, fungi such as Microsporum, Aspergillus [11], Chrysosporium [12], and Trichoderma [7] are also known to possess active keratinolytic properties. Enzymatic degradation of keratin using the enzymes isolated from bacterial cells or the bacterial culture has been proposed as a clean and inexpensive method of converting feather keratin into single amino acids and polypeptides.

Given the effectiveness of traditional mutagenesis for creating mutants that produce improved yields of several microbial enzymes, such as α-galactosidase and lipase [13], it is reasonable to assume that a similar strategy could be successfully applied to enhance the ability of keratinase-producing strains.

Response Surface Methodology (RSM) is an experimental approach that efficiently determines the optimal conditions for a multivariable approach [14, 15]. RSM is a collection of mathematical and analytical techniques that are used to design experiments, construct models, measure responses, analyze results, and explore relationships between controlled factors under experimental conditions. This methodology is particularly useful in bioprocess optimization for identifying the most favorable conditions.

Inter Simple Sequence Repeat (ISSR) markers are a PCR-based method that do not require any prior genomic information [16]. ISSR markers are highly polymorphic and can be used in studies on genetic diversity, phylogeny, gene tagging, genome map**, and evolutionary biology [17]. Molecular markers like ISSR provide additional tools for characterizing, differentiating, and assessing genetic variation in collections. The ISSR-PCR technique of DNA fingerprinting has been employed in this work. It utilizes arbitrary, multi-loci markers generated by PCR amplification with microsatellite primers, which are quick, cost-effective, and easy to analyze [18]. Due to their high level of polymorphism and reproducibility, this fingerprinting technique is widely used, particularly in plants [17, 19], yeasts [20], and bacteria [21].

Therefore, the objective of this study was to isolate a new keratinase-producing actinomycetes strain and apply sequential mutagenesis to enhance keratinase productivity.

Materials and methods

Collecting samples

A total of 10 poultry waste samples were collected from 10 different farmers in Cairo, Egypt, from a depth of 5–10 cm, in sterile containers. Authors have permission for collection of samples obtained from the farm owners.

Culture media and isolation of keratinase-producing bacteria

The basic medium used for isolation and fermentation of the feather-degrading bacteria was according to [22, 23]. Plate count (P.C) agar medium (Himedia, West Chester, Pennsylvania, USA) was used for actinomycetes growth.

Preparation of keratin solution

Soluble keratin was prepared from chicken feathers obtained from local poultry waste according to the method of [24, 25].

Keratinase production and enzyme assay

A pure single colony of freshly selected actinomycetes isolate, which was grown on P.C agar medium, was aseptically transferred for keratinase production using fermentation medium according to [23]. After incubation, the supernatant containing the enzyme excreted was used in quantitative keratinase assay. Keratinase enzyme activity was measured using keratin solution as a substrate. In brief, 1.0 ml of cell-free supernatant (crude enzyme) was incubated with 1 ml keratin in 0.05 M Tris–HCl buffer (pH 8.0) and incubated at 50 °C for 10 min as reported by [26]. One unit (U/ml) of keratinolytic activity was defined as an increase of corrected absorbance of 280 nm (A280) with the control for 0.01 per minute under the conditions described above and calculated by the following equation: U = 4 × n × A280/(0.01 × 10), where n is the dilution rate; 4 is the final reaction volume (ml); 10 is the incubation time (min).

Determination of protein content and residual hydrolysates

Protein content was determined as described by [27] using bovine serum albumin as a standard. The residual hydrolysates were composed of undigested feathers and cells. The residual hydrolysate’s weight was determined according to [23].

Molecular identification of keratinolytic actinomyces

DNA extraction: The extraction method was applied according to the manufacturer of (Applied Biotechnology Co., Egypt), the actinomyces isolate was identified based on partial sequencing of the 16S rRNA gene, using the universal primers as follow, forward primer 63f (5’- CAGGCCTAACACATGCAAGTC-3’) and reverse primer 1387R (5’- GGGCGGWGTGTACAAGGC-3’). PCR product was purified and sequenced as described previously [28]. PCR program was 95 °C for 3 min, 30 cycles of denaturation at 95 °C for 45 s, annealing at 56 °C for 45 s, and extension at 72 °C for 1 min /1kbp, final extension 72 °C for 5 min. PCR product was purified using MEGA Quick-Spin total fragment DNA purification kit as instructed by the manufacturer. Sequencing was performed by the Sanger method [29].The phylogenetic tree was constructed using MAFFT alignment [30]. https://mafft.cbrc.jp/alignment/server/phylogeny.html.

Mutagenesis

The wild-type S. werraensis KN23was cultured on P.C broth medium at 37 °C for 2-5 days. Then, 10 ml of culture were centrifuged at 9000 g at 4 °C for 10 min to separate the cell biomass. Then, the cell biomass pellet was resuspended in 10 ml of sterilized saline (0.9%).

For UV-induced mutagenesis [Full size image

Adequacy of the model

To ensure the accuracy of the model, a random set of 30 production combinations was used to experimentally retest the keratinase production [44, 46]. The optimized conditions determined from the model predicted a keratinase production of 129.60 U/ml. The experimental value obtained was 122.54 U/ml, which was close to the predicted value. The model was found to be valid and reliable, as confirmed by the close agreement between the experimental and predicted values, as indicated in Table 4.

Factor coding is Coded.

Sum of squares is Type III – Partial.

The Model F-value of 21.05 indicates that the model is significant, and there is only a 0.01% chance that such a large F-value could occur due to noise.

Model terms with P-values less than 0.0500 are considered significant, and in this case, A, B, AC, and A² are significant model terms. Model reduction may improve the model if there are many insignificant model terms (excluding those required to support hierarchy), with values greater than 0.1000.

The Lack of Fit F-value of 1.34 implies that the Lack of Fit is not significant relative to the pure error, and there is a 39.24% chance that a Lack of Fit F-value this large could occur due to noise. Non-significant Lack of Fit is desirable as it means that the model fits well.

The Predicted R² of 0.7798 is in reasonable agreement with the Adjusted R² of 0.9063; the difference is less than 0.2.

The Adeq Precision measures the signal to noise ratio, and a ratio greater than 4 is desirable. The ratio of 14.967 indicates an adequate signal, and this model can be used to navigate the design space.

Select the highest order polynomial where the additional terms are significant and the model is not aliased.

Molecular depiction

Molecular description of S.werraensis KN23 and its mutants were conducted using ISSR primers (ISSR-152, ISSR-153, ISSR-154, UBC851, ISSR-157, ISSR-158, and ISSR-861). The ISSR analysis produced a total of 122 markers, with 112 of them being polymorphic, and unique bands displaying 91% polymorphism, as shown in Table 8 and Fig. 7. The highest number of total bands were displayed in S. werraensis KN23 and S. A-18, with 51 bands, followed by the mutant S. A-27 with 50 bands, as indicated in Table 9 and Fig. 6. The number of polymorphic bands with unique markers was 41 for KN23 and SA-18, whereas the mutants S.A-27, S.A-31, and S.A-14 showed 40, 39, and 37 bands, respectively. The lowest number of total bands and polymorphic bands with unique markers were displayed in isolate SA-26, with 39 and 29 bands, respectively, compared to the other genotypes, as shown in Tables 9 and 10, and Figs. 6, 7 and 8.

Table 8 Band variation and polymorphism percentage in six mutants Streptomyces werraensis using seven ISSR primers

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Table 9 The total amplified fragments produced by each primer from Streptomyces werraensis strain and its five mutants

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Table 10 Polymorphic and unique amplified fragments produced by each primer from Streptomyces werraensisstrain and its five mutants

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Proximity matrix analysis (Genetic Similarity)

Data showed in Table 11 recorded (15) pairwise comparisons to debate the genetic relationships among 6 Streptomyces werraensis genotypes detected in terms of similarity. The genetic similarity ranged from (0.380 to 0.571) with an average of (0.475), where the biggest value of genetic similarity was (0.571) among (S.werraensisKN23 and SA-14) and the lowest value of similarity was (0.380) between (SA-18 and SA-31), respectively. Also, high genetic similarity values were observed within, (SA-14 and SA-18) (0.551), (S.werraensis KN23 and SA-18) (0.549), (SA-26 and SA-27) (0.539),While (SA-14 and SA-26) (SA-18 and SA-27) was (0.535) on the other hand (S.werraensis KN23 and SA-26) (0.489) (S.werraensisKN23 and SA-27) (0.475) (KN23 and SA-31) (0.420) finaly the (SA-14 and SA-31) (0.396), (SA-14 and SA-27) (0.392) and (SA-18 and SA-31) (0.380) respectively.

Table 11 Genetic similarity percentages for Streptomyces werraensis KN23 strain and its five mutants, using 5 ISSR Primers

Full size table

Cluster analysis (Phylogenetic tree)

Results of cluster analysis viewed in Fig. 9 divided all Streptomyces werraensis genotypes into two main clusters. The cluster I included (SA-31) only while, cluster II contained two sub-clusters. The sub-cluster one included (SA-27), the sub-cluster two included (SA-26). While, the sub-cluster two divided into two sub-sub clusters. The sub-sub cluster one included (SA-18), while the sub-sub cluster two included one group (SA-14 and S.werraensis KN23).

Discussion

Twenty-five Actinomyces isolates were obtained from ten different poultry farms and were tested for keratinase activity. Among these isolates, Streptomyces werraensis KN23, which was isolated from Cairo, Egypt, produced the highest amount of extracellular keratinase (51.60 U/ml) after 48 h. The strain of Streptomyces werraensis KN23 that produced the highest levels of keratinase was chosen. Mutation has been recognized as a tool for altering the DNA sequence of a specific gene, and mutagenesis is the source of genetic variants [35]. Numerous studies have been conducted to improve keratinase production using gene modification and mutation techniques. When using a conventional genetic approach to mutagenesis, random mutations are used to increase a wild strain's production of the desired metabolites [36]. Although the manufacture of industrial enzymes was significantly aided by the DNA recombinant approach, random mutagenesis is still preferred and frequently chosen as the best option due to consistent strain development [31]. The advantages of random mutagenesis using chemical mutagens are overwhelming due to their simplicity and low-cost procedure compared to recombinant DNA [35]. The improvement of bacterial enzyme production by random mutation has received considerable attention. Several methods have been included in the usage of physical mutagenesis, including Ɣ-rays, X-rays, ultraviolet (UV) rays, or chemical mutagenesis, such as Colchicine, hydrogen peroxide (H₂O₂), ethyl methane sulfonate (EMS), sodium azide (SA), and ethidium bromide (Eth.Br.). These have been mentioned as effective mutagens for increasing the keratinase level from producing strains [32, 33]. In this work, we reported the usage of three efficient mutagenesis methods, namely hydrogen peroxide (H₂O₂), sodium azide (SA), and ultraviolet (UV), as a tool to modify the original wild-type strain Streptomyces werraensis KN23 in multistep mutation induction for keratinase production improvement. This resulted in a collection of Streptomyces werraensis KN23 mutant strains, with one mutant, SA-27 at a concentration of 0.1% sodium azide treatment, giving (106.92 U/ml), compared with the wild type of Streptomyces werraensis KN23 (51.60 U/ml). Thus, the present study indicated that using H₂O₂, SA, and UV mutagenesis to induce mutation was in favor of keratinase production improvement. This simple method provided strains that produced more enzyme than the wild type from which it was derived.

These findings corroborate those of [47], who discovered a keratinase-producing yeast isolate known as Pichia kudriavzevii, which produced 164.04 U/ml. Highly keratinase-productive mutants were produced using a multistep mutation induction procedure with ultraviolet, ethidium bromide, and ethyl methane sulfonate (EMS) mutagenesis. The results demonstrated that EMS-37, which exhibited activity of 211.90 U/ml, was the most efficient keratinolytic mutant [32]. The maximum keratinase activity was found at pH 8–9 and 40–45 °C, utilizing white chicken feathers as the keratin substrate for 72 h, with a feather concentration of 0.5–1%. Five keratinolytic bacteria were identified from poultry farm waste. When keratinase activity was boosted using the chemical mutagen ethyl methane sulfonate (EMS) and/or the physical mutagen UV radiation, five mutants with 1.51–3.73-fold higher keratinase activity than the wild type were produced [33]. The keratinolytic activity of a newly identified Bacillus safensis and a mutant variation produced by UV treatment was tested [33]. Over the course of 120 h, the mutant produced 64.4 ± 108.5 U/mL of keratinase, while the wild-type generated 35.4 ± 50.4 U/mL. Bacillus subtilis was isolated from feather meal media using sodium nitrite and UV light. A mutant strain that can produce keratinase was found to be 75.9% more potent than the wild type [31]. Bacillus subtilis that produces keratinase was also isolated and its keratinase activity was increased via chemical mutagenesis with ethyl methane sulfonate (EMS) [48]. The best mutant found had keratinase activity of 27.44 U/ml, more than three times that of the wild type.

One of the aims of this study was to identify the optimal conditions for keratinase production by evaluating the effects of various improvement parameters. By employing this technique, we were able to enhance the environmental conditions necessary for optimal enzyme production. We used response surface methodology (RSM) to simultaneously investigate the primary and interaction effects of different environmental factors on keratinase production. The results revealed that the mutant strain SA-27 exhibited maximum keratinolytic activity at pH 7, after 72 h of incubation, with 1.5% sucrose and 1.5% yeast extract, producing 129.60 U/ml of keratinase (run 13). These findings support previous studies that have also utilized RSM to optimize enzyme production under various culture conditions.

These findings are consistent with [47]. RSM was used to optimize the culture conditions for the highly productive mutant yeast Pichia EMS-37, testing keratinase activity as a result of incubation time, pH, carbon sources, and nitrogen sources. The greatest keratinase activity was observed following response surface methodology adjustment of culture conditions for mutant EMS-37 at pH 5, 72 h of incubation period, 2.5% glucose, and 2.5% beef extract (as carbon and nitrogen sources), with an activity of 240.172 U/ml (Run3). Dutta and Banerjee [49] found that RSM can increase yield and lower the cost of enzyme production by using it to optimize the production of keratinase by Bacillus cereus. Sivakumar et al. [50] also improved the invertase production from A. niger cultivated on inexpensive agricultural wastes using this technology. Penicillium bilaiae produces an acidic protease that was optimized by [51] using the RSM approach. RSM is becoming more popular due to its aptitude for efficiently aggregating multivariable ideal conditions. RSM optimization of bioprocess approaches has been hailed as a successful method of identifying the ideal production process parameters [52]. RSM was also employed by [53] to enhance the performance of the laccase enzyme. After 5 days of optimization and incubation at 32 °C, the prospective white-rot fungus Penicillium chrysogenum showed its highest activity (7.9 U/ml) compared to the conventional approach. This procedure was more accurate.

The ISSR technique was used to study the genetic diversity and relationships between Streptomyces werraensis KN23 strain and its mutants. ISSR has also been successfully employed in phylogenetic and genetic diversity studies, as it is simple, inexpensive, and produces highly reproducible profiles. The genetic diversity of the Streptomyces werraensis KN23 wild type compared with five mutants was studied using ISSR. Molecular characterization using ISSR primers resulted in the production of a total of 122 markers, with 112 of them being polymorphic, and unique bands displaying 91% polymorphism. The highest number of total bands were displayed in S. werraensis KN23 and S.A-18 with 51 bands, followed by the mutant S.A-27 with 50 bands. The number of polymorphic unique bands were 41 in KN23 and SA-18, while the mutants S.A-27, S.A-31, and S.A-14 had 40, 39, and 37, respectively. The lowest number of total bands and polymorphic unique bands were displayed in isolate SA-26 with 39 and 29 bands, respectively, compared to the other genotypes. The dendrogram based on combined molecular data grouped the Streptomyces werraensis and mutants into two clusters. Results of cluster analysis divided all Streptomyces werraensis genotypes into two main clusters. Cluster I included (SA-31) only, while cluster II contained two sub-clusters. The sub-cluster one included (SA-27), and the sub-cluster two included (SA-26). The sub-cluster two was further divided into two sub-sub clusters. The sub-sub cluster one included (SA-18), while the sub-sub cluster two included one group (SA-14 and S. werraensis KN23).

The ISSR results are consistent with previous studies. For instance, [21] used ISSR to differentiate between probiotic lactic acid bacteria isolated from different sources of milk products. In addition, [54] studied the genetic diversity between different strains of UV Mutated Bacillus cereus bacteria for Transglutaminase production compared to wild type using ISSR. These findings are similar to [55], who studied the genetic difference between Desulfovibrio strains isolated from atmospheric soils in a sulfidogenic microbial community using ISSR and divided the strains into two clusters.

Conclusion

In the current study, several actinomycetes strains were assayed for keratinase production. Among them, Streptomyces werraensis KN23 showed the highest production of keratinase enzyme, recording 51.60 U/ml. Physical Ultraviolet (UV) and chemical mutagenesis (Sodium azide (SA) and hydrogen peroxide (H₂O₂) were employed to increase the expression of keratinase in Streptomyces werraensis KN23. A mutant strain, SA-27, exhibited significantly higher keratinolytic activity (106.92 U/ml) than its wild-type Streptomyces werraensis KN23 (51.60 U/ml). Response surface methodology was used for optimization of the microbial enzyme production by mutant SA-27, which resulted in a synergistic combination of effective parameter interactions. The optimized conditions for keratinase enzyme production were determined as pH 7, 72 h incubation time, 1.5% sucrose and 1.5% yeast extract, with a resulting activity of 129.60 U/ml. The findings of this study demonstrate a significant increase in extracellular keratinase production by mutant SA-27, which can have several industrial applications, particularly in the food industry. ISSR markers were used to study the genetic diversity and relationships between Streptomyces werraensis KN23 strain and its mutants.

Availability of data and materials

The datasets used and analyzed during the current study are available from the authors on reasonable request.

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Acknowledgements

The authors express their appreciation to National Research Centre, Cairo Egypt for his Continuous help

Funding

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This work was supported by Microbial Genetic Department, Genetics and Cytology Department, Biotechnology Research institute, National Research Centre, Dokki, Cairo, Egypt.

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Authors and Affiliations

Department of Microbial Genetics, National Research Centre, Dokki, 12622, Giza, Egypt
Nagwa M. Abd El-Aziz & Bigad E. Khalil
Genetics and Cytology Department, Biotechnology Research Institute, National Research Centre, 33 El Buhouth St, Cairo, 12622, Dokki, Egypt
Hayam Fouad Ibrahim

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Nagwa M. Abd El-Aziz
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Bigad E. Khalil
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Hayam Fouad Ibrahim
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Contributions

All authors suggested the research point, investigated the article. Nagwa M. Abd El-Aziz and Bigad E. Khalil planned the research methodology, conducted experimental procedures in isolation of different actinomyces strains from different poultry farms, assayed for keratinase activity. Identification using molecular identification by PCR and sequencing of 16S rRNA gene, improve the keratinase productivity of selected strain, sequential mutagenesis was applied. Optimization conditions of keratinase expression using the Response Surface Methodology was applied using different parameters. Contributed to data analysis and statistical analysis and data illustration. Participated in article writing these section, revising, and editing. Hayam Fouad Ibrahim planned the research methodology, conducted experimental procedures in the genetic diversity of wild type compared with five mutants were studied using Inter-simple sequence repeat ISSR. Contributed to data analysis and illustration, and participated in article writing, revising, and editing. All authors read and approved the article.

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Correspondence to Nagwa M. Abd El-Aziz.

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Abd El-Aziz, N.M., Khalil, B.E. & Ibrahim, H.F. Enhancement of feather degrading keratinase of Streptomyces swerraensis KN23, applying mutagenesis and statistical optimization to improve keratinase activity. BMC Microbiol 23, 158 (2023). https://doi.org/10.1186/s12866-023-02867-0

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Received: 19 December 2022
Accepted: 22 March 2023
Published: 30 May 2023
DOI: https://doi.org/10.1186/s12866-023-02867-0

Enhancement of feather degrading keratinase of Streptomyces swerraensis KN23, applying mutagenesis and statistical optimization to improve keratinase activity

Abstract

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Introduction

Materials and methods

Collecting samples

Culture media and isolation of keratinase-producing bacteria

Preparation of keratin solution

Keratinase production and enzyme assay

Determination of protein content and residual hydrolysates

Molecular identification of keratinolytic actinomyces

Mutagenesis

Adequacy of the model

Molecular depiction

Proximity matrix analysis (Genetic Similarity)

Cluster analysis (Phylogenetic tree)

Discussion

Conclusion

Availability of data and materials

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher's Note

Supplementary Information

Additional file 1.

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About this article

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