Production of Rifamycin using Amycolatopsis mediterranei (MTCC14)

Venkateswarlu, G.; Krishna, Pankaj; Rao, L. Venkateswar

doi:10.1007/pl00009033

Cited by 12 publications

(18 citation statements)

References 3 publications

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“…Our results were in agreement with the findings of many researchers [16,20,32] how found that the productivity increased by culture shaking in comparison with the static culture shaking in comparison with the static culture. Due to Streptomycetes are obligate aerobic organisms [21], while AL-Zahrani [34] found that the shaking culture reduced the productivity of Streptomyces J12.…”

Section: Agitation Ratesupporting

confidence: 93%

“…After the incubation period, the plates were examined for typical actinomycetes colonies, which had regular round, small, opaque, compact, frequently pigmented with white, brown, gray-pink, or other colors, the colonies were examined under a light microscope to observe their colonial morphology and distinguished from fungi colonies. The isolated actinomycetes were recultured in nutrient broth and nutrient agar slants and stored at 4°C for further study [20,22].…”

Section: Actinomycetes Isolationmentioning

confidence: 99%

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Optimization Production Conditions of Antibacterial Metabolite From Streptomyces Sp.

Al-Ghazali

Omran

2017

Asian J Pharm Clin Res

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Objectives: The paper aimed to isolate Streptomyces strain having the ability to produce antibacterial metabolites and optimize some environmental parameters for excellent antibiotic production.Methods: Different soil samples were collected from extreme environments of desert regions at Karbala Province, Iraq. Actinomycetes were isolated using different media. The primary screening for antibacterial production was accomplished, and the antibacterial activities were tested against pathogenic bacteria, including Escherichia coli, Staphylococcus aureus, Streptococcus agalactiae, and Pseudomonas aeruginosa. The most potent strain was chosen for optimizing some of environmental parameters to increase the bioactive metabolite production. Different parameters were studied such as culture media, temperature, pH, and agitation rate.Results: About eight Streptomyces strains were isolated from soil samples. All isolates appeared variable levels of antibiotic productions against Grampositive and negative pathogenic bacteria, and the best one was Streptomyces sp. LHR 9. The antibacterial metabolite production from Streptomyces sp. LHR 9 was affected by various cultural parameters. Glucose soybean meal broth as a fermentation medium at pH 7 yielded the highest antibiotic production under the optimal fermentation conditions, including the temperature at 35°C with 200 rpm (revolution/min) agitation rate and 7 days incubation period. Conclusion:The Streptomyces sp. LHR 9 showed antibacterial activity against both Gram-positive and negative pathogenic bacteria. It may consider as a potential source of drug production. Further study needs to purification and characterization of antibiotic and analyzes the mechanism for the antimicrobial activity of this bioactive compound.

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Section: Agitation Ratesupporting

confidence: 93%

Section: Actinomycetes Isolationmentioning

confidence: 99%

Optimization Production Conditions of Antibacterial Metabolite From Streptomyces Sp.

Al-Ghazali

Omran

2017

Asian J Pharm Clin Res

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“…Our results agreed with the findings of many researchers that found the antimicrobial productivity increased in shaking culture comparing with the static one. This is due to the fact that streptomycetes are obligate aerobic organisms [26,31,32].…”

Section: Effect Of Cultivation Methodsmentioning

confidence: 99%

Antibacterial Activity of Streptomyces Tunisiensis Mg520500 Isolated from Soil Samples

Moghannem

2018

The Asia Journal of Applied Microbiology

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“…They reported a twofold increase in Rifamycin B productivity to d 0.57 g L À1 . Venkateswarlu et al (1999) studied production of Rifamycin B with Amycolatopsis mediterranei (MTCC14) using carbon and locally available cheaper nitrogen sources. The Rifamycin B productivity was improved from 0.65 g L À1 to 1.4 g L À1 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of rifamycin B fermentation in shake flasks via a machine‐learning‐based approach

Bapat

Wangikar

2004

Biotech & Bioengineering

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Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions are possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity.

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Production of Rifamycin using Amycolatopsis mediterranei (MTCC14)

Cited by 12 publications

References 3 publications

Optimization Production Conditions of Antibacterial Metabolite From Streptomyces Sp.

Optimization Production Conditions of Antibacterial Metabolite From Streptomyces Sp.

Antibacterial Activity of Streptomyces Tunisiensis Mg520500 Isolated from Soil Samples

Optimization of rifamycin B fermentation in shake flasks via a machine‐learning‐based approach

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