Culture-based diversity of endophytic fungi of three species of Ferula grown in Iran.

A total of 1,348 endophytic fungal strains were isolated from Ferula ovina, F. galbaniflua, and F. persica. They included Eurotiales (16 species), Pleosporales (11 species), Botryosphaeriales (1 species), Cladosporiales (2 species), Helotiales (6 species), Hypocreales (31 species), Sordariales (7 species), Glomerellales (2 species), and Polyporales (1 species). F. ovina had the richest species composition of endophytic fungi, and the endophytic fungi were most abundant in their roots compared to shoots. Chao, Margalef, Shannon, Simpson, Berger-Parker, Menhinick, and Camargo indices showed that F. ovina roots had the most endophytic fungal species. The frequency distribution of fungal species isolated from Ferula spp. fell into the log-series model, and F. ovina roots had the highest Fisher alpha. The dominance indices showed that there are no dominant species in the endophytic fungal community isolated from Ferula spp., indicating community stability. Evenness values were 0.69, 0.90, 0.94, and 0.57 for endophytic fungi isolated from F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots, respectively, indicating a species distribution that tends toward evenness. The fungal species community isolated from each of F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots was a diverse species group originating from a homogeneous habitat. Their distribution followed a log-normal distribution, suggesting that the interactions of numerous independent environmental factors multiplicatively control species abundances. Principal component analysis showed that the highest species diversity and dominance were observed in the endophytic fungal community isolated from F. ovina and F. persica roots, respectively.

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint.

Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d-1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture.

BACKGROUND: Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. RESULTS: GRNN-FOA models (0.88-0.97) showed the superior prediction performances as compared to regression models (0.57-0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l-1). CONCLUSIONS: Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

The Critical Role of AtPAP17 and AtPAP26 Genes in Arabidopsis Phosphate Compensation Network.

Purple acid phosphatases (PAP)-encoding genes form a complex network that play a critical role in plant phosphate (Pi) homeostasis. Mostly, the functions of PAPs were investigated individually. However, the interactions of most of these genes in response to various concentrations of available Pi remain unknown. In this study, the roles of AtPAP17 and AtPAP26 genes, and their relationship within Pi homeostasis context were investigated. Surprisingly, atpap17 and atpap26 mutants not only showed no obvious developmental defects, but also produced higher biomass in compare to wild type (WT) plants under normal growth conditions. Comparing gene expression patterns of these mutants with WT plant, we identified a set of genes up-regulated in mutant plants but not in WT. Based on these unexpected results and up-regulation of AtPAP17 and AtPAP26 genes by the loss of function of each other, the hypothesis of compensation relationship between these genes in Pi homeostasis was assessed by generating atpap17/atpap26 double mutants. Observation of developmental defects in atpap17/atpap26 mutant but not in single mutants indicated a compensation relationship between AtPAP17 and AtPAP26 genes in Pi homeostasis network. Taken together, these results demonstrate the activation of AtPAP17 and AtPAP26 genes to buffer against the loss of function of each other, and this compensation relationship is vital for Arabidopsis growth and development.

Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods.

Paclitaxel as a microtubule-stabilizing agent is widely used for the treatment of a vast range of cancers. Corylus avellana cell suspension culture (CSC) is a promising strategy for paclitaxel production. Elicitation of paclitaxel biosynthesis pathway is a key approach for improving its production in cell culture. However, optimization of this process is time-consuming and costly. Modeling of paclitaxel elicitation process can be helpful to predict the optimal condition for its high production in cell culture. The objective of this study was modeling and forecasting paclitaxel biosynthesis in C. avellana cell culture responding cell extract (CE), culture filtrate (CF) and cell wall (CW) derived from endophytic fungus, either individually or combined treatment with methyl-ß-cyclodextrin (MBCD), based on four input variables including concentration levels of fungal elicitors and MBCD, elicitor adding day and CSC harvesting time, using adaptive neuro-fuzzy inference system (ANFIS) and multiple regression methods. The results displayed a higher accuracy of ANFIS models (0.94-0.97) as compared to regression models (0.16-0.54). The great accordance between the predicted and observed values of paclitaxel biosynthesis for both training and testing subsets support excellent performance of developed ANFIS models. Optimization process of developed ANFIS models with genetic algorithm (GA) showed that optimal MBCD (47.65 mM) and CW (2.77% (v/v)) concentration levels, elicitor adding day (16) and CSC harvesting time (139 h and 41 min after elicitation) can lead to highest paclitaxel biosynthesis (427.92 µg l-1). The validation experiment showed that ANFIS-GA method can be a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis, as a case study.

Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm.

Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89-0.95) than regression models (0.56-0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.

Whole fungal elicitors boost paclitaxel biosynthesis induction in Corylus avellana cell culture.

Paclitaxel is an effective natural-source chemotherapeutic agent commonly applied to treat a vast range of cancers. In vitro Corylus avellana culture has been reported as a promising and inexpensive system for paclitaxel production. Fungal elicitors have been made known as the most efficient strategy for the biosynthesis induction of secondary metabolites in plant in vitro culture. In this research, C. avellana cell suspension culture (CSC) was exposed to cell extract (CE) and culture filtrate (CF) derived from Camarosporomyces flavigenus, either individually or combined treatment, in mid and late log phase. There is no report on the use of whole fungal elicitors (the combined treatment of CE and CF) for the elicitation of secondary metabolite biosynthesis in plant in vitro culture. The combined treatment of CE and CF significantly led to more paclitaxel biosynthesis and secretion than the individual use of them. Also, multivariate statistical approaches including stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR) were used to model and predict paclitaxel biosynthesis and secretion. Based on value account for (VAF), root mean square error (RMSE), coefficient of determination (R2), mean absolute percentage error (MAPE) and relative percent difference (RPD) can be concluded that mentioned regression models effectively worked only for modeling and predicting extracellular paclitaxel portion in C. avellana cell culture.

Fungal Cell Wall and Methyl-ß-Cyclodextrin Synergistically Enhance Paclitaxel Biosynthesis and Secretion in Corylus avellana Cell Suspension Culture.

Paclitaxel is the top-selling chemotherapeutic drug used for the treatment of lung, ovarian and breast cancer as well as Kaposi's sarcoma. Cell suspension culture (CSC) of Corylus avellana has been addressed as a promising alternative for producing paclitaxel. In this study, endophytic fungus strain YEF33 was isolated from Taxus baccata and identified as Coniothyrium palmarum. The effects of the elicitors derived from this fungus including cell extract, culture filtrate and cell wall (CW) and also chitin, alone or in combination with Methyl-ß-Cyclodextrin (MBCD), on paclitaxel biosynthesis in C. avellana CSC were assayed for the first time. CW of C. palmarum was the most efficient fungal elicitor for paclitaxel biosynthesis in C. avellana CSC. The results revealed that MBCD affected paclitaxel biosynthesis differently depending on fungal elicitor type and vice versa. MBCD, either alone or in combination with fungal elicitors, induced a high secretion of paclitaxel, suggesting the decrement of toxicity and retro-inhibition processes of paclitaxel for cells. The joint effects of C. palmarum CW (2.5% (v/v) on 17th day) and 50 mM MBCD synergistically enhanced paclitaxel biosynthesis (402.4 µg l-1; 5.8-fold), 78.6% of which (316.5 µg l-1) were secreted into culture medium, a level 146% higher than that in control.

New synergistic co-culture of Corylus avellana cells and Epicoccum nigrum for paclitaxel production.

Paclitaxel is a main impressive chemotherapeutic agent with unique mode of action and broad-spectrum activity against cancers. Hazel (Corylus avellana) is a paclitaxel-producing species through bioprospection. Endophytic fungi have significant roles in plant paclitaxel production. This study evaluated the effect of co-culture of C. avellana cells and paclitaxel-producing endophytic fungus, Epicoccum nigrum strain YEF2 and also the effect of elicitors derived from this fungal strain on paclitaxel production. The results clearly revealed that co-culture of C. avellana cells and E. nigrum was more effective than elicitation of C. avellana cells by only cell extract or culture filtrate of this fungal strain. Co-culture of C. avellana cells and E. nigrum surpassed monocultures in terms of paclitaxel production designating their synergistic interaction potential. Fungal inoculum amount, co-culture establishment time and co-culture period were important factors for achieving the maximum production of paclitaxel in this co-culture system. The highest total yield of paclitaxel (404.5 µg L-1) was produced in co-culture established on 13th day using 3.2% (v/v) of E. nigrum mycelium suspension, which was about 5.5 and 136.6 times that in control cultures of C. avellana cells and E. nigrum, respectively. This is the first report on positive effect of co-culture of paclitaxel-producing endophytic fungus and non-host plant cells for enhancing paclitaxel production.