Axial skeletal defects caused by mutation in the spondylocostal dysplasia/pudgy gene Dll3 are associated with disruption of the segmentation clock within the presomitic mesoderm

A loss-of-function mutation in the mouse delta-like3 (Dll3) gene has been generated following gene targeting, and results in severe axial skeletal defects. These defects, which consist of highly disorganised vertebrae and costal defects, are similar to those associated with the Dll3-dependent pudgy mutant in mouse and with spondylocostal dysplasia (MIM 277300) in humans. This study demonstrates that Dll3neo and Dll3pu are functionally equivalent alleles with respect to the skeletal dysplasia, and we suggest that the three human DLL3 mutations associated with spondylocostal dysplasia are also functionally equivalent to the Dll3neo null allele. Our phenotypic analysis of Dll3neo/Dll3neo mutants shows that the developmental origins of the skeletal defects lie in delayed and irregular somite formation, which results in the perturbation of anteroposterior somite polarity. As the expression of Lfng, Hes1, Hes5 and Hey1 is disrupted in the presomitic mesoderm, we suggest that the somitic aberrations are founded in the disruption of the segmentation clock that intrinsically oscillates within presomitic mesoderm

Role of anesthesiology curriculum in improving bag-mask ventilation and intubation success rates of emergency medicine residents: a prospective descriptive study

<p>Abstract</p> <p>Background</p> <p>Rapid and safe airway management has always been of paramount importance in successful management of critically ill and injured patients in the emergency department. The purpose of our study was to determine success rates of bag-mask ventilation and tracheal intubation performed by emergency medicine residents before and after completing their anesthesiology curriculum.</p> <p>Methods</p> <p>A prospective descriptive study was conducted at Nikoukari Hospital, a teaching hospital located in Tabriz, Iran. In a skills lab, a total number of 18 emergency medicine residents (post graduate year 1) were given traditional intubation and bag-mask ventilation instructions in a 36 hour course combined with mannequin practice. Later the residents were given the opportunity of receiving training on airway management in an operating room for a period of one month which was considered as an additional training program added to their Anesthesiology Curriculum. Residents were asked to ventilate and intubate 18 patients (Mallampati class I and ASA class I and II) in the operating room; both before and after completing this additional training program. Intubation achieved at first attempt within 20 seconds was considered successful. Successful bag-mask ventilation was defined as increase in ETCo₂to 20 mm Hg and back to baseline with a 3 L/min fresh gas-flow and the adjustable pressure limiting valve at 20 cm H₂O. An attending anesthesiologist who was always present in the operating room during the induction of anesthesia confirmed the endotracheal intubation by direct laryngoscopy and capnography. Success rates were recorded and compared using McNemar, marginal homogeneity and paired t-Test tests in SPSS 15 software.</p> <p>Results</p> <p>Before the additional training program in the operating room, the participants had intubation and bag-mask ventilation success rates of 27.7% (CI 0.07-0.49) and 16.6% (CI 0-0.34) respectively. After the additional training program in the operating room the success rates increased to 83.3% (CI 0.66-1) and 88.8% (CI 0.73-1), respectively. The differences in success rates were statistically significant (P = 0.002 and P = 0.0004, respectively).</p> <p>Conclusions</p> <p>The success rate of emergency medicine residents in airway management improved significantly after completing anesthesiology rotation. Anesthesiology rotations should be considered as an essential component of emergency medicine training programs. A collateral curriculum of this nature should also focus on the acquisition of skills in airway management.</p

Inhibition of SLPI ameliorates disease activity in experimental autoimmune encephalomyelitis

<p>Abstract</p> <p>Background</p> <p>The secretory leukocyte protease inhibitor (SLPI) exerts wide ranging effects on inflammatory pathways and is upregulated in EAE but the biological role of SLPI in EAE, an animal model of multiple sclerosis is unknown</p> <p>Methods</p> <p>To investigate the pathophysiological effects of SLPI within EAE, we induced SLPI-neutralizing antibodies in mice and rats to determine the clinical severity of the disease. In addition we studied the effects of SLPI on the anti-inflammatory cytokine TGF-β.</p> <p>Results</p> <p>The induction of SLPI neutralizing antibodies resulted in a milder disease course in mouse and rat EAE. SLPI neutralization was associated with increased serum levels of TGF-β and increased numbers of FoxP3+ CD4+ T cells in lymph nodes. <it>In vitro</it>, the addition of SLPI significantly decreased the number of functional FoxP3+ CD25^hiCD4+ regulatory T cells in cultures of naive human CD4+ T cells. Adding recombinant TGF-β to SLPI-treated human T cell cultures neutralized SLPI's inhibitory effect on regulatory T cell differentiation.</p> <p>Conclusion</p> <p>In EAE, SLPI exerts potent pro-inflammatory actions by modulation of T-cell activity and its neutralization may be beneficial for the disease.</p

A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

<p>Abstract</p> <p>Background</p> <p>In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.</p> <p>Results</p> <p>Instead of the two traditional methods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate single, master, and modified calibration curves. The mean and the percentiles of the posterior distribution were used as point and interval estimates of unknown parameters such as intercepts, slopes and DNA concentrations. The software WinBUGS was used to perform all simulations and to generate the posterior distributions of all the unknown parameters of interest.</p> <p>Conclusion</p> <p>The Bayesian approach defined in this study allowed for the estimation of DNA concentrations from environmental samples using absolute standard curves generated by real-time qPCR. The approach accounted for uncertainty from multiple sources such as experiment-to-experiment variation, variability between replicate measurements, as well as uncertainty introduced when employing calibration curves generated from absolute plasmid DNA standards.</p

Regulation of Gene Expression in Plants through miRNA Inactivation

Eukaryotic organisms possess a complex RNA-directed gene expression regulatory network allowing the production of unique gene expression patterns. A recent addition to the repertoire of RNA-based gene regulation is miRNA target decoys, endogenous RNA that can negatively regulate miRNA activity. miRNA decoys have been shown to be a valuable tool for understanding the function of several miRNA families in plants and invertebrates. Engineering and precise manipulation of an endogenous RNA regulatory network through modification of miRNA activity also affords a significant opportunity to achieve a desired outcome of enhanced plant development or response to environmental stresses. Here we report that expression of miRNA decoys as single or heteromeric non-cleavable microRNA (miRNA) sites embedded in either non-protein-coding or within the 3′ untranslated region of protein-coding transcripts can regulate the expression of one or more miRNA targets. By altering the sequence of the miRNA decoy sites, we were able to attenuate miRNA inactivation, which allowed for fine regulation of native miRNA targets and the production of a desirable range of plant phenotypes. Thus, our results demonstrate miRNA decoys are a flexible and robust tool, not only for studying miRNA function, but also for targeted engineering of gene expression in plants. Computational analysis of the Arabidopsis transcriptome revealed a number of potential miRNA decoys, suggesting that endogenous decoys may have an important role in natural modulation of expression in plants

A Primer on Regression Methods for Decoding cis-Regulatory Logic

The rapidly emerging field of systems biology is helping us to understand the molecular determinants of phenotype on a genomic scale [1]. Cis-regulatory elements are major sequence-based determinants of biological processes in cells and tissues [2]. For instance, during transcriptional regulation, transcription factors (TFs) bind to very specific regions on the promoter DNA [2,3] and recruit the basal transcriptional machinery, which ultimately initiates mRNA transcription (Figure 1A). Learning cis-Regulatory Elements from Omics Data A vast amount of work over the past decade has shown that omics data can be used to learn cis-regulatory logic on a genome-wide scale [4-6]--in particular, by integrating sequence data with mRNA expression profiles. The most popular approach has been to identify over-represented motifs in promoters of genes that are coexpressed [4,7,8]. Though widely used, such an approach can be limiting for a variety of reasons. First, the combinatorial nature of gene regulation is difficult to explicitly model in this framework. Moreover, in many applications of this approach, expression data from multiple conditions are necessary to obtain reliable predictions. This can potentially limit the use of this method to only large data sets [9]. Although these methods can be adapted to analyze mRNA expression data from a pair of biological conditions, such comparisons are often confounded by the fact that primary and secondary response genes are clustered together--whereas only the primary response genes are expected to contain the functional motifs [10]. A set of approaches based on regression has been developed to overcome the above limitations [11-32]. These approaches have their foundations in certain biophysical aspects of gene regulation [26,33-35]. That is, the models are motivated by the expected transcriptional response of genes due to the binding of TFs to their promoters. While such methods have gathered popularity in the computational domain, they remain largely obscure to the broader biology community. The purpose of this tutorial is to bridge this gap. We will focus on transcriptional regulation to introduce the concepts. However, these techniques may be applied to other regulatory processes. We will consider only eukaryotes in this tutorial

First and second eye cataract surgery and driver self-regulation among older drivers with bilateral cataract: A prospective cohort study

Background: Driving a car is the most common form of transport among the older population. Common medical conditions such as cataract, increase with age and impact on the ability to drive. To compensate for visual decline, some cataract patients may self-regulate their driving while waiting for cataract surgery. However, little is known about the self-regulation practices of older drivers throughout the cataract surgery process. The aim of this study is to assess the impact of first and second eye cataract surgery on driver self-regulation practices, and to determine which objective measures of vision are associated with driver self-regulation. Methods: Fifty-five older drivers with bilateral cataract aged 55+ years were assessed using the self-reported Driving Habits Questionnaire, the Mini-Mental State Examination and three objective visual measures in the month before cataract surgery, at least one to three months after first eye cataract surgery and at least one month after second eye cataract surgery. Participants' natural driving behaviour in four driving situations was also examined for one week using an in-vehicle monitoring device. Two separate Generalised Estimating Equation logistic models were undertaken to assess the impact of first and second eye cataract surgery on driver-self-regulation status and which changes in visual measures were associated with driver self-regulation status. Results: The odds of being a self-regulator in at least one driving situation significantly decreased by 70% after first eye cataract surgery (OR: 0.3, 95% CI: 0.1-0.7) and by 90% after second eye surgery (OR: 0.1, 95% CI: 0.1-0.4), compared to before first eye surgery. Improvement in contrast sensitivity after cataract surgery was significantly associated with decreased odds of self-regulation (OR: 0.02, 95% CI: 0.01-0.4). Conclusions: The findings provide a strong rationale for providing timely first and second eye cataract surgery for older drivers with bilateral cataract, in order to improve their mobility and independence

Computational analyses of eukaryotic promoters

Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005

Machine learning for regulatory analysis and transcription factor target prediction in yeast

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data