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LC-MS-based metabolomics study of marine bacterial secondary metabolite and antibiotic production in Salinispora arenicola.

Bose U, Hewavitharana AK, Ng YK, Shaw PN, Fuerst JA, Hodson MP - Mar Drugs (2015)

Bottom Line: An LC-MS-based metabolomics approach was used to characterise the variation in secondary metabolite production due to changes in the salt content of the growth media as well as across different growth periods (incubation times).We used metabolomics as a tool to investigate the production of rifamycins (antibiotics) and other secondary metabolites in the obligate marine actinobacterial species Salinispora arenicola, isolated from Great Barrier Reef (GBR) sponges, at two defined salt concentrations and over three different incubation periods.The results indicated that a 14 day incubation period is optimal for the maximum production of rifamycin B, whereas rifamycin S and W achieve their maximum concentration at 29 days.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, The University of Queensland, Brisbane, Queensland 4072, Australia. utpal.bose@uqconnect.edu.au.

ABSTRACT
An LC-MS-based metabolomics approach was used to characterise the variation in secondary metabolite production due to changes in the salt content of the growth media as well as across different growth periods (incubation times). We used metabolomics as a tool to investigate the production of rifamycins (antibiotics) and other secondary metabolites in the obligate marine actinobacterial species Salinispora arenicola, isolated from Great Barrier Reef (GBR) sponges, at two defined salt concentrations and over three different incubation periods. The results indicated that a 14 day incubation period is optimal for the maximum production of rifamycin B, whereas rifamycin S and W achieve their maximum concentration at 29 days. A "chemical profile" link between the days of incubation and the salt concentration of the growth medium was shown to exist and reliably represents a critical point for selection of growth medium and harvest time.

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Chemoinformatic analyses of S. arenicola strains after 29 days growth in SYP-1% NaCl and SYP-3% NaCl. (A) PCA scores plot of PC1 versus PC2 generated from day 29 samples (N = 18); (B) OPLS-DA scores plot derived from day 29 samples (N = 18), supervised by % NaCl. Further analyses of individual strains are presented in Supplementary Figure S4A–C; (C) Loadings S-plot derived from (B). Data are log10 transformed and mean centred. Red highlighted variables are, from left to right: RifSV—Rifamycin SV; RifW—Rifamycin W; RifP—Rifamycin P; Stau—Staurosporine.
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marinedrugs-13-00249-f005: Chemoinformatic analyses of S. arenicola strains after 29 days growth in SYP-1% NaCl and SYP-3% NaCl. (A) PCA scores plot of PC1 versus PC2 generated from day 29 samples (N = 18); (B) OPLS-DA scores plot derived from day 29 samples (N = 18), supervised by % NaCl. Further analyses of individual strains are presented in Supplementary Figure S4A–C; (C) Loadings S-plot derived from (B). Data are log10 transformed and mean centred. Red highlighted variables are, from left to right: RifSV—Rifamycin SV; RifW—Rifamycin W; RifP—Rifamycin P; Stau—Staurosporine.

Mentions: PCA generated a four component model that explained 62% of the variance in the dataset. The first two component scores of the model are shown in (Figure 5A). OPLS-DA was then used to refine the model fit and partition the variance into predictive and orthogonal sources. The first predictive and orthogonal components are plotted in (Figure 5B); 19% of the variance in secondary metabolites was related to two different salt concentrations (one predictive component), whereas 32.1% of the variance was unrelated to the effect of salt concentration (two orthogonal components). Model metrics are presented in Supplementary Table S1. For the comparison of two salt concentrations (1% and 3% NaCl), twelve compounds were tentatively identified and classified according to the PCDL (Table 1).


LC-MS-based metabolomics study of marine bacterial secondary metabolite and antibiotic production in Salinispora arenicola.

Bose U, Hewavitharana AK, Ng YK, Shaw PN, Fuerst JA, Hodson MP - Mar Drugs (2015)

Chemoinformatic analyses of S. arenicola strains after 29 days growth in SYP-1% NaCl and SYP-3% NaCl. (A) PCA scores plot of PC1 versus PC2 generated from day 29 samples (N = 18); (B) OPLS-DA scores plot derived from day 29 samples (N = 18), supervised by % NaCl. Further analyses of individual strains are presented in Supplementary Figure S4A–C; (C) Loadings S-plot derived from (B). Data are log10 transformed and mean centred. Red highlighted variables are, from left to right: RifSV—Rifamycin SV; RifW—Rifamycin W; RifP—Rifamycin P; Stau—Staurosporine.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4306935&req=5

marinedrugs-13-00249-f005: Chemoinformatic analyses of S. arenicola strains after 29 days growth in SYP-1% NaCl and SYP-3% NaCl. (A) PCA scores plot of PC1 versus PC2 generated from day 29 samples (N = 18); (B) OPLS-DA scores plot derived from day 29 samples (N = 18), supervised by % NaCl. Further analyses of individual strains are presented in Supplementary Figure S4A–C; (C) Loadings S-plot derived from (B). Data are log10 transformed and mean centred. Red highlighted variables are, from left to right: RifSV—Rifamycin SV; RifW—Rifamycin W; RifP—Rifamycin P; Stau—Staurosporine.
Mentions: PCA generated a four component model that explained 62% of the variance in the dataset. The first two component scores of the model are shown in (Figure 5A). OPLS-DA was then used to refine the model fit and partition the variance into predictive and orthogonal sources. The first predictive and orthogonal components are plotted in (Figure 5B); 19% of the variance in secondary metabolites was related to two different salt concentrations (one predictive component), whereas 32.1% of the variance was unrelated to the effect of salt concentration (two orthogonal components). Model metrics are presented in Supplementary Table S1. For the comparison of two salt concentrations (1% and 3% NaCl), twelve compounds were tentatively identified and classified according to the PCDL (Table 1).

Bottom Line: An LC-MS-based metabolomics approach was used to characterise the variation in secondary metabolite production due to changes in the salt content of the growth media as well as across different growth periods (incubation times).We used metabolomics as a tool to investigate the production of rifamycins (antibiotics) and other secondary metabolites in the obligate marine actinobacterial species Salinispora arenicola, isolated from Great Barrier Reef (GBR) sponges, at two defined salt concentrations and over three different incubation periods.The results indicated that a 14 day incubation period is optimal for the maximum production of rifamycin B, whereas rifamycin S and W achieve their maximum concentration at 29 days.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, The University of Queensland, Brisbane, Queensland 4072, Australia. utpal.bose@uqconnect.edu.au.

ABSTRACT
An LC-MS-based metabolomics approach was used to characterise the variation in secondary metabolite production due to changes in the salt content of the growth media as well as across different growth periods (incubation times). We used metabolomics as a tool to investigate the production of rifamycins (antibiotics) and other secondary metabolites in the obligate marine actinobacterial species Salinispora arenicola, isolated from Great Barrier Reef (GBR) sponges, at two defined salt concentrations and over three different incubation periods. The results indicated that a 14 day incubation period is optimal for the maximum production of rifamycin B, whereas rifamycin S and W achieve their maximum concentration at 29 days. A "chemical profile" link between the days of incubation and the salt concentration of the growth medium was shown to exist and reliably represents a critical point for selection of growth medium and harvest time.

Show MeSH
Related in: MedlinePlus