Combined use of LC–ESI-MS and antifungal tests for rapid identification of bioactive lipopeptides produced by Bacillus amyloliquefaciens CCMI 1051

The strain Bacillus amyloliquefaciens CCMI 1051 used in this study has been isolated in our laboratory from healthy Quercus suber in the south of Portugal and shows high levels of antagonistic properties against filamentous fungi that attack forest products industry due to the production of bioactive peptides. A combined use of LC–ESI-MS and antifungal tests allowed a rapid identification of lipopeptides as active compounds produced. Applying autobiographic methods it was possible to obtain active compounds. LC–ESI-MS, a powerful tool for rapid identification, indicates the presence of lipopeptides and MS2 electrospray ionization showed the partial sequence Tyr–Asn–Pro–Glu in the peptidic portion of some compounds produced. The association of mass spectrometry and chromatography, used in parallel with antifungal tests proved to be an efficient approach for the characterization of active lipopeptides without the need of previous total isolation. This methodology can be employed for screening and optimization the production of antifungal iturinic lipopeptides, showing a great potential for future application

Molecular evaluation of some Amanita ponderosa and fungal strains living in association with these mushrooms in the south western Iberian Peninsula

Amanita ponderosa are wild edible mushrooms that grow only in some microclimates, particularly those in the southwestern part of the Iberian Peninsula. Due to the vast diversity of mushrooms in nature, as well as nutrient variability, which is highly dependent on soil type and environmental conditions, it is essential to be able to characterize fungal microbiota that lives in association with mushrooms and to differentiate A. ponderosa strains of different regions for certification purposes. In this study, we characterized the genetic profile of A. ponderosa mushrooms and the fungal strains that live in association with them in their natural habitat and compared the fingerprinting profiles obtained by M13-PCR amplification of the genomic DNA.We found that the predominant fungal isolates living in association with A. ponderosa were Aspergillus spp., Penicillium spp. and Mucor spp. M13-PCR molecular analysis showed that different fungal isolates had different genetic profiles. This approach allowed us to differentiate the different fungi strains isolated from fruiting bodies of A. ponderosa both rapidly and in a reproducible manner and to group them according to genus. Our fingerprinting analyses also distinguished different A. ponderosa mushrooms collected from different regions. Consequently, we conclude that this method is a very discriminatory approach for differentiating both A. ponderosa from different sites and the fungal microbiota that lives in association with these mushrooms

Modelling of Bacillus Amyloliquefaciens CCMI 1051 Cultures Using Artificial Intelligence Based Tools

It is well known that Bacillus species produce a wide variety of metabolites with interesting biological activities, namely antibiotic compounds as iturinic lipopeptides, being the aspartic acid a favourable nitrogen source for iturinic compounds production by B. subtilis and by B. amyloliquefaciens. The incubation time is another factor to be considered on antibiotic production. On the other hand, Artificial Neural Networks (ANN) are widely accepted as a tool that offers an alternative way to tackle complex problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. The prediction of Bacillus sporulation (BS) and antifungal activity of compounds (AFA), from incubation time of cultures (IT) and from aspartic acid concentration (AA) is a complex and highly nonlinear problem for which there are no known methods to predict them directly and accurately. The aim of this study is to optimize the production of antifungal compounds in B. amyloliquefaciens CCMI 1051 cultures using ANN. The database to be used contains antifungal data of cultures with different IT (1-9 days) using AA (0.4-5.6 g/L) as nitrogen source. In order to obtain the best prediction of the AFA and BS, different network structures and architectures have to be elaborated. The optimum number of hidden layers and the optimum number of nodes in each of these will be found by trial and error. The model being depicted above was in mean time accomplished, and the results obtained with it appointed that the maximum AFA is achieved with 2.6 g/L of aspartic acid on day 9. However, with AA of 4.8 g/L a similar maximum value of activity is obtained for incubation time over 6 days. The model shows a dual behaviour for AFA, depending of the IT. When the IT is higher than 5 days the AFA versus AA shows a pronounced sigmoid profile, converging to a common maximum value of AFA. On the other hand, for IT lower than 5 days mentioned profile is ill-defined and the common converging point isn’t observed. The conclusion is that the use of ANNs show to be a potent computational tool that must be present in any intelligent predictive task applied to Bacillus cultures, evidencing nitrogen source as key factor to be considered in these kind of problems, where the time of incubation plays a role in secondary production of active compounds

Molecular biomarkers and inorganic profile to characterize Amanita ponderosa mushrooms strains

Amanita ponderosa are wild mushroom eatable, very appreciated in gastronomy, with high exportation potential, that grow spontaneously in some microclimates, particularly in Alentejo and Andalusia. Due to the symbiotic relation, mushrooms can accumulate high concentrations of some metals, consequently is important to estimate the trace metal contents to assessing exposure risks. Although some species of genera Amanita are toxic, others are edible and very appreciate, namely A. ponderosa that grows spontaneously in southwest of the Iberian Peninsula. Therefore is important to improve methods to characterize different Amanita strains. In this study, we intend to evaluate the inorganic composition (P, Na, K, Ca, Mg, Fe, Cu, Zn and Mn content) of several Amanita ponderosa strains and to characterise these mushrooms with molecular biomarkers. A. ponderosa strains showed different inorganic profile according to their locate area. The amplified DNA polymorphic sequences analyses by MSP-PCR allowed to identify at specie level and to differentiate A. ponderosa at strain from each origin sites

Modelling molecular and inorganic data of Amanita ponderosa mushrooms using artificial neural networks

Amanita ponderosa are wild mushroom eatable, growing spontaneously in some Mediterranean microclimates, namely in Alentejo and Andaluzia, in the Iberian Peninsula, due to its Mediterranean characteristics. The aim of this study was to evaluate inorganic composition of mycorrhizal Amanita ponderosa collected from different regions of the southwest of the Iberian Peninsula and to access molecular biomarkers using artificial neural networks. Fruiting bodies of the A. ponderosa mushrooms were collected in Spring from different locations area, in the southwest of the Iberian Peninsula. Three individuals were sampled per location. The inorganic analyses showed that mineral composition of these mushrooms depends on the ecosystem where they grow. Levels of trace metals are considerably lower, acceptable to human consumption at nutritional and low toxic levels. Molecular approach using the microsatellite primer M13-PCR allowed to distinguish the mushrooms at specie level and to differentiate the A. ponderosa strains according to their location. Data mining tools were used in order to correlate inorganic and molecular results. In order to obtain the best prediction of the M13 PCR DNA band profile, different network structures and architectures were elaborated and evaluated. In the present work the error metric used was the mean squared error. The neural network selected for modelling the data has a 6-7-14 topology, i.e. an input layer with six nodes, a hidden layer with seven nodes and a fourteen nodes output layer. A good match between the experimental and predicted values can be observed

A Data Mining Approach to Characterize Amanita ponderosa Mushrooms Using Inorganic Profile and M13-PCR Molecular Data

Wild eatable mushrooms Amanita ponderosa are very appreciated in gastronomy, showing high export potential. This specie grows spontaneously in some microclimates, namely in the southwest of the Iberian Peninsula. The aim of this study is to find inorganic and molecular markers that allow to characterize the wild A. ponderosa strains collected from different geographical locations in the Iberian Peninsula. Molecular approach using the microsatellite primer M13-PCR allowed to distinguish the mushrooms at specie level and to differentiate the A. ponderosa strains according to their location. Data mining tools were used in order to correlate inorganic and molecular results. A. ponderosa strains showed different inorganic composition according to their habitat. It was developed a segmentation model based on the molecular analysis, which allow relating the clusters obtained with the geographical site of sampling. There were also developed explanatory models of the segmentation, using decision trees, by following two different strategies. One of them based on the bands of DNA and, the other one, based on the mineral composition. The results show that it may be possible to relate the molecular and inorganic data. The present findings are wide potential application and both health and economical benefits arise from this study

Redução de compostos fenólicos de resíduos de lagares de azeite utilizando culturas de Coriolus versicolor

In this study, C. versicolor cultures were performed during 20 days with different RLA (olive mill wastes)subtracts, 50%, 75% and 100%. During this assay, samples were collected in regular time intervals, cell growth were evaluated and broth decolorization was observed. The organic matter and total phenolic compounds level in the culture broth showed a decrease in all RLA ratios. 50% and 75% RLA presented high total phenolic removal, 85% and 90% respectively. In the end of the assay, toxicological evaluation of the culture broth was performed against Artemia salina. These results showed a toxicological decrease of these residues after biological treatment with C. Versicolor

Aroma Compounds Prevision using Artificial Neural Networks Influence of Newly Indigenous Saccharomyces SPP in White Wine Produced with Vitis Vinifera Cv Siria

Commercial yeasts strains of Saccharomyces cerevisae are frequently used in white wine production as starters in fermentation process, however, these strains can affect the wine characteristics. The aim of this study was to evaluate the effect of three strains of Saccharomyces spp. (var. 1, 2 and 3) on wine aroma compounds produced in microvinification assays. Microvinification assays were carried out with Vitis vinifera cv Síria grapes using the strains in study as starters. Aroma compounds were identified and quantified by GC-FID and GC-MS. At the end of fermentation process and during the first three months of maturation some aroma compounds were detected, namely propanol, isobutanol, isoamyl acetate, isoamylic alcohol, ethyl hexanoate, ethyl lactate, hexanol, ethyl octanoate, 3-ethylhydroxibutirate, benzaldehyde, 3-methyl-2-butanol, 2,3-butanediol, g-butyrolactone, ethyl decanoate, diethyl succinate, methionol, 4-hydroxi-2-butyrolactone, heptanoic acid, phenylethyl acetate, ethyl dodecanoate, phenylethanol, octanoic acid, 2-methoxy-4- vinylphenol and decanoic acid. Artificial Neural Networks (ANNs) were used to predict the concentration of twelve wine aroma compounds from the phenyl ethanol, propanol, isobutanol, hexanol, heptanoic acid, octanoic acid and decanoic acid concentrations. Results showed that, either, maturation time and Saccharomyces strain used as starter influence the aroma compounds produced. Wines produced with S. cerevisae var. 1 and S. cerevisae var. 2 showed a similar composition in aroma compounds, relatively to the wines produced with the strain S. cerevisae var. 3. However, for S. cerevisae var. 1 and S. cerevisae var. 2 the time of maturation influence the aroma composition of wines. From a technological approach, the choice of yeast strain and maturation time has decisive influence on the aroma compounds produced