Bioinformatic characterization of the extracellular lipases from Kluyveromyces marxianus

Lipases are hydrolytic enzymes that break the ester bonds of triglycerides, generating free fatty acids and glycerol. Extracellular lipase activity has been reported for the nonconventional yeast Kluyveromyces marxianus, grown in olive oil as a substrate, and the presence of at least eight putative lipases has been detected in its genome. However, to date, there is no experimental evidence on the physiological role of the putative lipases nor their structural and catalytic properties. In this study, a bioinformatic analysis of the genes of the putative lipases from K. marxianus L-2029 was performed, particularly identifying and characterizing the extracellular expected enzymes, due to their biotechnological relevance. The amino acid sequence of 10 putative lipases, obtained by in silico translation, ranged between 389 and 773 amino acids. Two of the analysed putative proteins showed a signal peptide, 25 and 33 amino acids long for KmYJR107Wp and KmLIP3p, and a molecular weight of 44.53 and 58.23 kDa, respectively. The amino acid alignment of KmLIP3p and KmYJR107Wp with the crystallized lipases from a patatin and the YlLip2 lipase from Yarrowia lipolytica, respectively, revealed the presence of the hydrolase characteristic motifs. From the 3D models of putative extracellular K. marxianus L-2029 lipases, the conserved pentapeptide of each was determined, being GTSMG for KmLIP3p and GHSLG for KmYJR107Wp; besides, the genes of these two enzymes (LIP3 and YJR107W) are apparently regulated by oleate response elements. The phylogenetic analysis of all K. marxianus lipases revealed evolutionary affinities with lipases from abH15.03, abH23.01, and abH23.02 families.     

Hydrogen peroxide concentration measured in cultivation substrates during growth and fruiting of the mushrooms Agaricus bisporus and Pleurotus spp.

Hydrogen peroxide is suspected of being highly implicated in mushroom nutrition and in substrate bleaching during cultivation. The parameters for measuring H₂O₂ in compost samples were examined and the methodology was applied to samples from both compost colonized by cultivars and wild isolates of Agaricus bisporus, and wheat straw or coffee pulp colonized by Pleurotus spp. Laccase and peroxidase activities were also measured. H₂O₂ concentration measured after heating at 80 °C for inactivating laccases and peroxidases was probably both H₂O₂ pre‐existing in the compost and H₂O₂ generated from quinones and active oxygen species. This potential H₂O₂ concentration increased during the vegetative growth for all the strains, in agreement with a direct relationship between H₂O₂ concentration and active biomass of A. bisporus or Pleurotus spp. in their cultivation substrates. Correlations were observed between H₂O₂ concentration and manganese peroxidase activity in cultivation substrates at the stage of primordia formation. At this stage of development, H₂O₂ generation via biotic or abiotic mechanisms should be an important physiological trait of mushrooms. Copyright © 2007 Society of Chemical Industry     

Wavelet Denoising of TSD Deflection Slope Measurements for Improved Pavement Structural Evaluation

Continuous deflection devices (CDDs) can safely measure pavement deflection (or other related properties) while traveling at highway speed, which reduces traffic disruption. CDD measurements are contaminated with relatively high noise levels compared to stop‐and‐go devices such as the Falling Weight Deflectometer. In this article, we use wavelet transform denoising to remove the noise and estimate the true deflection slope measurements obtained from the Traffic Speed Deflectometer. Results show that failure to denoise deflection slope measurements can lead to calculated Effective Structural Number values that are highly variable (unstable). Attempting to filter these highly variable measurements can lead to erroneous results. We also use wavelet transform denoising to identify localized weak spots such as those that are caused by pavement reflection cracking. Identifying weak spots with wavelets is possible because wavelets are spatially adaptive to local features. In contrast, a linear filter is not capable of adapting to local features.