A gene expression study on strains of Nostoc (Cyanobacteria) revealing antimicrobial activity under mixotrophic conditions

Cyanobacteria are well known for their production of a multitude of highly allelopathic compounds. These products have features such as incorporation of non-proteinogenic amino acids which are characteristics of peptides biosynthesized by non-ribosomal peptide synthetases (NRPSs). Some of these peptides have acetate-derived moieties, suggesting that their biosynthesis also involves polyketide synthases (PKSs). Among the photosynthetic microorganisms, cyanobacteria belonging to the genus Nostoc are regarded as good candidates for producing biologically active secondary metabolites. Aiming at the maximization in the production of natural product, we compared autotrophic, and mixotrophic growth at high light intensity of two Nostoc species in relation to the production of bioactive compounds with the antimicrobial activity at different source of sugar. Glucose was shown to be the best substrate for the production of high natural product when compared with sucrose. Also, the rate of biomass production and antimicrobial activity was reaching ~2.0 to 2.5 and ~1.5 times greater than that of the autotrophic and sucrose grown cultures, respectively. Also, we conduct a combined NRPSs and PKSs polymerase chain reaction (PCR). The sequences presented in this study was deposited in GenBank and had accession numbers JF795278 and JF795279 (NRPS A domains) and JF795280 and JF795281 (PKS KS domains). Computer modeling and phylogenetic analysis was conducted to predict the putative amino acid recognized by the unknown adenylation domain in the NRPS sequences. This study highlights the importance of environmental and nutrimental factors in maximization of antibiotic production of two Nostoc species.Keywords: Peptide synthetase gene, polyketide synthase gene, Nostoc, secondary metabolites, mixotrophic condition

Optimization of growth media components for polyhydroxyalkanoate (PHA) production from organic acids by Ralstonia eutropha

We employed systematic mixture analysis to determine optimal levels of acetate, propionate, and butyrate for cell growth and polyhydroxyalkanoate (PHA) production by Ralstonia eutropha H16. Butyrate was the preferred acid for robust cell growth and high PHA production. The 3-hydroxyvalerate content in the resulting PHA depended on the proportion of propionate initially present in the growth medium. The proportion of acetate dramatically affected the final pH of the growth medium. A model was constructed using our data that predicts the effects of these acids, individually and in combination, on cell dry weight (CDW), PHA content (%CDW), PHA production, 3HV in the polymer, and final culture pH. Cell growth and PHA production improved approximately 1.5-fold over initial conditions when the proportion of butyrate was increased. Optimization of the phosphate buffer content in medium containing higher amounts of butyrate improved cell growth and PHA production more than 4-fold. The validated organic acid mixture analysis model can be used to optimize R. eutropha culture conditions, in order to meet targets for PHA production and/or polymer HV content. By modifying the growth medium made from treated industrial waste, such as palm oil mill effluent, more PHA can be produced.Malaysia. Ministry of Science, Technology and Innovation (MOSTI

Data mining models to predict ocean wave energy flux in the absence of wave records

Ocean wave energy is known as a renewable energy resource with high power potential and without negative environmental impacts. Wave energy has a direct relationship with the ocean’s meteorological parameters. The aim of the current study is to investigate the dependency between ocean wave energy flux and meteorological parameters by using data mining methods (DMMs). For this purpose, a feed-forward neural network (FFNN), a cascade-forward neural network (CFNN), and gene expression programming (GEP) are implemented as different DMMs. The modeling is based on historical meteorological and wave data taken from the National Data Buoy Center (NDBC). In all models, wind speed, air temperature, and sea temperature are input parameters. In addition, the output is the wave energy flux which is obtained from the classical wave energy flux equation. It is notable that, initially, outliers in the data sets were removed by the local distribution based outlier detector (LDBOD) method to obtain the best and most accurate results. To evaluate the performance and accuracy of the proposed models, two statistical measures, root mean square error (RMSE) and regression coefficient (R), were used. From the results obtained, it was found that, in general, the FFNN and CFNN models gave a more accurate prediction of wave energy from meteorological parameters in the absence of wave records than the GEP method