Metabolite fingerprinting of novel Streptomyces UK-238 from the Himalayan forest

2020 ◽  
Vol 16 ◽  
Author(s):  
Nidhi Srivastava ◽  
Indira P. Sarethy

Aims: Characterization of antimicrobial metabolites of novel Streptomyces sp. UK-238. Background: Novel antimicrobial drug discovery is urgently needed due to emerging multi antimicrobial drug resistance among pathogens. Since many years, natural products have provided the basic skeletons for many therapeutic compounds including antibiotics. Bioprospection of un/under explored habitats and focussing on selective isolation of actinobacteria as major reservoir of bio and chemodiversity has yielded good results. Objective: The main objectives of the study were the identification of UK-238 by 16S rDNA sequencing and antimicrobial metabolite fingerprinting of culture extracts. Method: In the present study, a promising isolate, UK-238, has been screened for antimicrobial activity and metabolite fingerprinting from the Himalayan Thano Reserve forest. It was identified by 16S rDNA sequencing. Ethyl acetate extract was partially purified by column chromatography. The pooled active fractions were fingerprinted by GC-MS and compounds were tentatively identified by collated data analysis based on Similarity Index, observed Retention Index from Databases and calculated Retention Index. Results: UK-238 was identified as Streptomyces sp. with 98.4% similarity to S. niveiscabiei. It exhibited broad-spectrum antibacterial and antifungal activity. GC-MS analysis of active fractions of ethyl acetate extract showed the presence of eighteen novel antimicrobial compounds belonging to four major categories- alcohols, alkaloid, esters and peptide. Conclusion: The study confirms that bioprospection of underexplored habitats can elaborate novel bio and chemodiversity.

2019 ◽  
Vol 09 ◽  
Author(s):  
Nidhi Srivastava ◽  
Sanjay Gupta ◽  
Indira P. Sarethy

Background: Multi-drug resistance among pathogens is emerging due to slow pace of development of new antimicrobials by combinatorial chemistry. Natural products from microorganisms from under-explored habitats can be lead molecules for such discoveries. Objective: The major objectives were to characterize isolate UK-201, taxonomically identify UK-201 based on 16S rDNA sequencing and execute metabolite fingerprinting of ethyl acetate extract of UK-201 by GC-MS. Method: In search of new antimicrobial compounds, Streptomyces isolate UK-201 exhibiting broad spectrum antimicrobial and antifungal activity, obtained from under-explored Lachhiwala Reserve forest, of the Himalayas was selected in this study. Isolate UK-201 was identified by 16S rDNA sequencing. Ethyl acetate extract of this isolate exhibited antimicrobial activity against all selected panel of Gram positive, Gram negative bacteria and fungi among other organic solvent extracts. Hence, EA extract was partially purified by column chromatography. Active fractions were pooled and analysed by GC-MS. Obtained compounds were tentatively identified by collated data analysis based on Similarity Index, observed Retention Index from Databases and calculated Retention Index. Results: Isolate UK-201 showed 97.46% similarity to Streptomyces niveiscabiei, 96.88% to S. sasae and S. puniciscabiei, 96.72% to S. capoamus and S. yaanensis. Low similarity percentage indicated the taxonomic novelty of the isolate and was confirmed by comparing with phenotypic characteristics with nearest matches. Metabolite fingerprinting showed the presence of twenty-four compounds and could be novel. Conclusion: This study showed that bioprospection from under-explored habitats conferred novel bio and chemodiversity.


2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Weston J. Jackson ◽  
Ipsita Agarwal ◽  
Itsik Pe’er

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.


2020 ◽  
Vol 40 (4) ◽  
Author(s):  
Luying Shan ◽  
Yinjiao Li ◽  
Shi Zheng ◽  
Yuanmiao Wei ◽  
Ying Shang

Author(s):  
Jaiganesh R ◽  
Jaganathan Mk

Objective: The objective of this work was to isolation, purification and characterization of solvent tolerant lipase from Bacillus sp. The objective of this work was to isolation, purification and characterization of solvent tolerant lipase from Bacillus sp. from kitchen grease for a variety of applications including organic synthetic reactions and preparation of enantiomerically pure pharmaceuticals.Methods: Lipase producing isolates were screened from kitchen grease on a selective medium rhodamine B olive oil agar, and tributyrin agar was used to screen the lipase and esterase producing an organism, respectively. The isolate identified using 16S rDNA sequencing method and enzyme activity was quantitatively assayed. Lipase production was characterized in different conditions.Results: The isolate showed highest lipase activity was which later was identified as Bacillus sp. using 16S rDNA sequencing method. The lipase was purified using ammonium sulfate precipitation. The isolate showed excellent tolerance to methanol, ethanol, acetonitrile, and moderate tolerance to butanol. The increased biomass concentration, maximum production, and activity were achieved at 37°C in 24 h incubation, then gradual reduction in production was observed. The maximum activity of lipase enzyme was obtained at pH between 6 and 9.Conclusion: The isolate produce solvent tolerance lipase enzyme and it can be a promising candidate of solvent tolerance lipase enzyme for variety of industrial applications.


2016 ◽  
Vol 206 ◽  
pp. 66-72 ◽  
Author(s):  
Jian-Lei Gu ◽  
Yi-Zhong Wang ◽  
Shi-Yi Liu ◽  
Guang-Jun Yu ◽  
Ting Zhang ◽  
...  

2020 ◽  
Vol 10 ◽  
pp. 100102
Author(s):  
Hao Chen ◽  
Kaiqiang Fu ◽  
Binbin Pang ◽  
Jifang Wang ◽  
Huatao Li ◽  
...  

Microbiology ◽  
1999 ◽  
Vol 145 (7) ◽  
pp. 1797-1807 ◽  
Author(s):  
Denis O. Krause ◽  
Brian P. Dalrymple ◽  
Wendy J. Smith ◽  
Roderick I. Mackie ◽  
Christopher S. McSweeney

2015 ◽  
Vol 22 (6) ◽  
pp. 744-751 ◽  
Author(s):  
Sung-Kwon Lee ◽  
Dong-Ryung Lee ◽  
Bong-Keun Choi ◽  
Sasikumar Arunachalam Palaniyandi ◽  
Seung Hwan Yang ◽  
...  

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