Cloning, heterologous expression, and characterization of a novel thioesterase from natural sample.

A novel thioesterse gene was successfully cloned and sequenced directly from natural sample of Domas Hot Spring, West Java, Indonesia. Homological analysis of the sequence showed that the gene appeared high homology to thioesterase genes with the highest to a putative thioesterase gene from uncultured Acidilobus sp. JCHS at 66% identity. However, phylogenetic analysis showed that the protein was separated from the branch with other known thioesterases. The size of the gene is around 500 base pairs, lied into 2 kb DNA fragment from a random PCR amplicon. The gene was overexpressed in Escherichia coli, a dominant band appeared at 17 kDa in SDS-PAGE with expression level at around 32% of total proteins. The activity of the purified protein using acetyl-CoA as substrate showed that the protein exhibited thioesterase activity. Furthermore, the enzyme also showed esterase activity on p-nitrophenyl ester as substrate. Detail characterization of esterolytic activity showed that the enzyme preferred p-nitrophenyl decanoate as substrate. The optimum activity of the enzyme was at 80 °C and pH 8. Activity of the enzyme was maintained after incubation at 80 °C up to 24 h. In addition, the enzyme was favorable on polar organic solvents. All the data obtained suggested that the enzyme is a novel alkaline thermostable thioesterase.

Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA.

The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA ("ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning" (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements.