Cloning of taxadiene synthase gene into Arabidopsis thaliana (ecotype Columbia-0)

Paclitaxel (Taxol), a complex diterpenoid, produced by yew tree (Taxus sp.) is the most important chemotherapeutic agent that is widely used against a variety of malignancies such as ovarian and breast cancers. However, destructive methods for its production from natural resources together with currently used low-yielding industrial production systems via total synthesis or semi-synthesis have led researchers to invent a robust alternative biological production system using biotechnological approaches. The first committed step in taxol biosynthesis pathway is the  production of taxadiene from geranylgeranyl diphosphate (GGPP) catalyzed by the plastid-localized enzyme taxadiene synthase (TXS). In this research, an attempt was made to evaluate the effects of the first critical enzyme in thetaxol biosynthesis pathway on Arabidopsis plant through the expression of taxadiene synthase gene under the control of a dexamethasone-inducible promoter. To achieve this goal, Arabidopsis plants (ecotype Columbia-0) were transformed with the construct pTA-TXS-His via floral dip method using Agrobacterium tumefaciens AGL1. The transformed plants were confirmed using the PCR reaction amplifying an 800 bp fragment of the cloned gene. Upon these findings, a proposal was made that biotechnological strategies could be utilized for the production of taxol components

KMT2B Is Selectively Required for Neuronal Transdifferentiation, and Its Loss Exposes Dystonia Candidate Genes

Transdifferentiation of fibroblasts into induced neuronal cells (iNs) by the neuron-specific transcription factors Brn2, Myt1l, and Ascl1 is a paradigmatic example of inter-lineage conversion across epigenetically distant cells. Despite tremendous progress regarding the transcriptional hierarchy underlying transdifferentiation, the enablers of the concomitant epigenome resetting remain to be elucidated. Here, we investigated the role of KMT2A and KMT2B, two histone H3 lysine 4 methylases with cardinal roles in development, through individual and combined inactivation. We found that Kmt2b, whose human homolog's mutations cause dystonia, is selectively required for iN conversion through suppression of the alternative myocyte program and induction of neuronal maturation genes. The identification of KMT2B-vulnerable targets allowed us, in turn, to expose, in a cohort of 225 patients, 45 unique variants in 39 KMT2B targets, which represent promising candidates to dissect the molecular bases of dystonia. Barbagiovanni et al. demonstrate that KMT2B, in contrast to KMT2A, is fundamental for the epigenetic and transcriptomic resetting underlying transdifferentiation of fibroblasts into induced neuronal cells (iNs), acting both in the suppression of alternative fates and in the promotion of iN maturation. Transdifferentiation-specific KMT2B targets reveal dystonia-causative gene candidates

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

ATR expands embryonic stem cell fate potential in response to replication stress

Unrepaired DNA damage during embryonic development can be potentially inherited by a large population of cells. However, the quality control mechanisms that minimize the contribution of damaged cells to developing embryos remain poorly understood. Here, we uncovered an ATR-and CHK1-mediated transcriptional response to replication stress (RS) in mouse embryonic stem cells (ESCs) that induces genes expressed in totipotent two-cell (2C) stage embryos and 2C-like cells. This response is mediated by Dux, a multicopy retrogene defining the cleavage-specific transcriptional program in placental mammals. In response to RS, DUX triggers the transcription of 2C-like markers such as murine endogenous retrovirus-like elements (MERVL) and Zscan4. This response can also be elicited by ETAA1-mediated ATR activation in the absence of RS. ATR-mediated activation of DUX requires GRSF1-dependent post-transcriptional regulation of Dux mRNA. Strikingly, activation of ATR expands ESCs fate potential by extending their contribution to both embryonic and extra-embryonic tissues. These findings define a novel ATR dependent pathway involved in maintaining genome stability in developing embryos by controlling ESCs fate in response to RS

Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets