The use of artificial MicroRNA technology to control gene expression in Arabidopsis thaliana

In plants, double-stranded RNA (dsRNA) is an effective trigger of RNA silencing, and several classes of endogenous small RNA (sRNA), processed from dsRNA substrates by DICER-like (DCL) endonucleases, are essential in controlling gene expression. One such sRNA class, the microRNAs (miRNAs) control the expression of closely related genes to regulate all aspects of plant development, including the determination of leaf shape, leaf polarity, flowering time, and floral identity. A single miRNA sRNA silencing signal is processed from a long precursor transcript of nonprotein-coding RNA, termed the primary miRNA (pri-miRNA). A region of the pri-miRNA is partially self-complementary allowing the transcript to fold back onto itself to form a stem-loop structure of imperfectly dsRNA. Artificial miRNA (amiRNA) technology uses endogenous pri-miRNAs, in which the miRNA and miRNA*(passenger strand of the miRNA duplex) sequences have been replaced with corresponding amiRNA/ amiRNA*sequences that direct highly efficient RNA silencing of the targeted gene. Here, we describe the rules for amiRNA design, as well as outline the PCR and bacterial cloning procedures involved in the construction of an amiRNA plant expression vector to control target gene expression in Arabidopsis thaliana. © 2014 Springer Science+Business Media New York.

A 22-nt artificial microRNA mediates widespread RNA silencing in Arabidopsis

It is known that 22-nucleotide (nt) microRNAs (miRNAs) derived from asymmetric duplexes trigger phased small-interfering RNA (phasiRNA) production from complementary targets. Here we investigate the efficacy of 22-nt artificial miRNA (amiRNA)-mediated RNA silencing relative to conventional hairpin RNA (hpRNA) and 21-nt amiRNA-mediated RNA silencing. CHALCONE SYNTHASE (CHS) was selected as a target in Arabidopsis thaliana due to the obvious and non-lethal loss of anthocyanin accumulation upon widespread RNA silencing. Over-expression of CHS in the pap1-D background facilitated visual detection of both local and systemic RNA silencing. RNA silencing was initiated in leaf tissues from hpRNA and amiRNA plant expression vectors under the control of an Arabidopsis RuBisCo small subunit 1A promoter (SSU). In this system, hpRNA expression triggered CHS silencing in most leaf tissues but not in roots or seed coats. Similarly, 21-nt amiRNA expression from symmetric miRNA/miRNA* duplexes triggered CHS silencing in all leaf tissues but not in roots or seed coats. However, 22-nt amiRNA expression from an asymmetric duplex triggered CHS silencing in all tissues, including roots and seed coats, in the majority of plant lines. This widespread CHS silencing required RNA-DEPENDENT RNA POLYMERASE6-mediated accumulation of phasiRNAs from the endogenous CHS transcript. These results demonstrate the efficacy of asymmetric 22-nt amiRNA-directed RNA silencing and associated phasiRNA production and activity, in mediating widespread RNA silencing of an endogenous target gene. Asymmetric 22-nt amiRNA-directed RNA silencing requires little modification of existing amiRNA technology and is expected to be effective in suppressing other genes and/or members of gene families.

Vectors and methods for hairpin RNA and artificial microRNA-mediated gene silencing in plants

In plant cells, DICER-LIKE4 processes perfectly double-stranded RNA (dsRNA) into short interfering (si) RNAs, and DICER-LIKE1 generates micro (mi) RNAs from primary miRNA transcripts (pri-miRNA) that form fold-back structures of imperfectly dsRNA. Both si and miRNAs direct the endogenous endonuclease, ARGONAUTE1 to cleave complementary target single-stranded RNAs and either small RNA (sRNA)-directed pathway can be harnessed to silence genes in plants. A routine way of inducing and directing RNA silencing by siRNAs is to express self-complementary single-stranded hairpin RNA (hpRNA), in which the duplexed region has the same sequence as part of the target gene's mRNA. Artificial miRNA (amiRNA)-mediated silencing uses an endogenous pri-miRNA, in which the original miRNA/miRNA* sequence has been replaced with a sequence complementary to the new target gene. In this chapter, we describe the plasmid vector systems routinely used by our research group for the generation of either hpRNA-derived siRNAs or amiRNAs.

Efficient silencing of endogenous microRNAs using artificial microRNAs in Arabidopsis thaliana

We report here that the expression of endogenous microRNAs (miRNAs) can be efficiently silenced in Arabidopsis thaliana (Arabidopsis) using artificial miRNA (amiRNA) technology. We demonstrate that an amiRNA designed to target a mature miRNA directs silencing against all miRNA family members, whereas an amiRNA designed to target the stem-loop region of a miRNA precursor transcript directs silencing against only the individual family member targeted. Furthermore, our results indicate that amiRNAs targeting both the mature miRNA and stem-loop sequence direct RNA silencing through cleavage of the miRNA precursor transcript, which presumably occurs in the nucleus of a plant cell during the initial stages of miRNA biogenesis. This suggests that small RNA (sRNA)-guided RNA cleavage in plants occurs not only in the cytoplasm, but also in the nucleus. Many plant miRNA gene families have been identified via sequencing and bioinformatic analysis, but, to date, only a small tranche of these have been functionally characterized due to a lack of effective forward or reverse genetic tools. Our findings therefore provide a new and powerful reverse-genetic tool for the analysis of miRNA function in plants. © The Author 2010. Published by the Molecular Plant Shanghai Editorial Office in association with Oxford University Press on behalf of CSPP and IPPE, SIBS, CAS.

Perceptions of organ donors and willingness to donate organs upon death : a test of the prototype/willingness model

Understanding people's organ donation decisions may narrow the gap between organ supply and demand. In two studies, participants who had not recorded their posthumous organ donation decision (Study 1, N = 210; Study 2, N = 307) completed items assessing prototype/willingness model (PWM; attitude, subjective norm, donor prototype favorability and similarity, willingness) constructs. Attitude, subjective norm, and prototype similarity predicted willingness to donate. Prototype favorability and a Prototype Favorability × Similarity interaction predicted willingness (Study 2). These findings provide support for the PWM in altruistic health contexts, highlighting the importance of people's perceptions about organ donors in their donation decisions.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

Recent advances in the design of artificial corneas

Purpose of review: Artificial corneas are being developed to meet a shortage of donor corneas as well as to address cases where allografting is contraindicated. A range of artificial corneas has been developed. Here we review several newer designs and especially those inspired by naturally occurring biomaterials found with the human body and elsewhere. Recent findings: Recent trends in the development of artificial corneas indicate a move towards the use of materials derived from native sources including decellularized corneal tissue and tissue substitutes synthesized by corneal cells in vitro when grown either on their own, or in conjunction with novel protein-based scaffolds. Biologically inspired materials are also being considered for implantation on their own with the view to promoting endogenous corneal tissue. Summary: More recent attempts at making artificial corneas have taken a more nature-based or nature-inspired approach. Several will in the near future be likely to be available clinically.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Integrin-functionalized artificial membranes as test platforms for monitoring small integrin ligand binding by surface plasmon-enhanced fluorescence spectroscopy

The design and synthesis of molecularly or supramolecularly defined interfacial architectures have seen in recent years a remarkable growth of interest and scientific research activities for various reasons. On the one hand, it is generally believed that the construction of an interactive interface between the living world of cells, tissue, or whole organisms and the (inorganic or organic) materials world of technical devices such as implants or medical parts requires proper construction and structural (and functional) control of this organism–machine interface. It is still the very beginning of generating a better understanding of what is needed to make an organism tolerate implants, to guarantee bidirectional communication between microelectronic devices and living tissue, or to simply construct interactive biocompatibility of surfaces in general. This exhaustive book lucidly describes the design, synthesis, assembly and characterization, and bio-(medical) applications of interfacial layers on solid substrates with molecularly or supramolecularly controlled architectures. Experts in the field share their contributions that have been developed in recent years.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

Activation of matrix metalloproteinase-2 (MMP-2) by membrane type 1 matrix metalloproteinase through an artificial receptor for ProMMP-2 generates active MMP-2

The suggested model for pro-matrix metalloproteinase-2 (proMMP-2) activation by membrane type 1 MMP (MT1-MMP) implicates the complex between MT1-MMP and tissue inhibitor of MMP-2 (TIMP-2) as a receptor for proMMP-2. To dissect this model and assess the pathologic significance of MMP-2 activation, an artificial receptor for proMMP-2 was created by replacing the signal sequence of TIMP-2 with cytoplasmic/transmembrane domain of type II transmembrane mosaic serine protease (MSP-T2). Unlike TIMP-2, MSP-T2 served as a receptor for proMMP-2 without inhibiting MT1-MMP, and generated TIMP-2-free active MMP-2 even at a low level of MT1-MMP. Thus, MSP-T2 did not affect direct cleavage of the substrate testican-1 by MT1-MMP, whereas TIMP-2 inhibited it even at the level that stimulates proMMP-2 processing. Expression of MSP-T2 in HT1080 cells enhanced MMP-2 activation by endogenous MT1-MMP and caused intensive hydrolysis of collagen gel. Expression of MSP-T2 in U87 glioma cells, which express a trace level of endogenous MT1-MMP, induced MMP-2 activation and enhanced cell-associated protease activity, activation of extracellular signal-regulated kinase, and metastatic ability into chick embryonic liver and lung. MT1-MMP can exert both maximum MMP-2 activation and direct cleavage of substrates with MSP-T2, which cannot be achieved with TIMP-2. These results suggest that MMP-2 activation by MT1-MMP potentially amplifies protease activity, and combination with direct cleavage of substrate causes effective tissue degradation and enhances tumor invasion and metastasis, which highlights the complex role of TIMP-2. MSP-T2 is a unique tool to analyze physiologic and pathologic roles of MMP-2 and MT1-MMP in comparison with TIMP-2.

Human breast cancer cell metastasis to long bone and soft organs of nude mice : a quantitative assay

Bone is a common metastatic site in human breast cancer (HBC). Since bone metastasis occurs very rarely from current spontaneous or experimental metastasis models of HBC cells in nude mice, an arterial seeding model involving the direct injection of the cells into the left ventricle has been developed to better understand the mechanisms involved in this process. We present here a sensitive polymerase chain reaction (PCR) method to detect and quantitate bone and soft organ metastasis in nude mice which have been intracardially inoculated with Lac Z transduced HBC cells. Amplification of genomically incorporated Lac Z sequences in MDA-MB-231-BAG HBC cells enables us to specifically detect these cells in mouse organs and bones. We have also created a competitive template to use as an internal standard in the PCR reactions, allowing us to better quantitate levels of HBC metastasis. The results of this PCR detection method correlate well with cell culture detection from alternate long bones from the same mice, and are more sensitive than gross Lac Z staining with X-gal or routine histology. Comparable qualitative results were obtained with PCR and culture in a titration experiment in which mice were inoculated with increasing numbers of cells, but PCR is more quantifiable, less time consuming, and less expensive. This assay can be employed to study the molecular and cellular aspects of bone metastasis, and could easily be used in conjunction with RT-PCR-based analyses of gene products which may be involved with HBC metastasis.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Cells Tissues Organs : preface

This volume stems from the 1st International Conference on Epithelial-Mesenchymal Transitions (EMT), which was convened by the editors on October 5–8, 2003 in the beautiful setting of Port Douglas, Queensland, Australia. EMT, the name given to the transformation of cells arranged in a coherent layer – epithelial cells – to more individualistic and potential motile cells – mesenchymal cells – was recognized decades ago by Prof. Elisabeth (Betty) Hay (Harvard Medical School, Boston, Mass., USA) as a primary mechanism in embryogenesis for remodelling tissues. More recently EMT has been seen as crucial to the spread and invasion of carcinoma, and more recently still, EMT-like changes have been detected in various pathologies marked by fi brosis. Despite the basic and clinical importance of EMT, this extremely rapidly growing fi eld had never previously had a conference devoted to it, and indeed the disciplines of developmental biology, cancer and pathology rarely interact although they have much to share. The chapters assembled for this volume encompass these three major themes of the meeting, development, pathology and cancer, and further highlight the commonality in terms of mechanisms and outcomes among them...