Novel critical point drying (CPD) based preparation and transmission electron microscopy (TEM) imaging of protein specific molecularly imprinted polymers (HydroMIPs)

We report the transmission electron microscopy (TEM) imaging of a hydrogel-based molecularly imprinted polymer (HydroMIP) specific to the template molecule bovine haemoglobin (BHb). A novel critical point drying based sample preparation technique was employed to prepare the molecularly imprinted polymer (MIP) samples in a manner that would facilitate the use of TEM to image the imprinted cavities, and provide an appropriate degree of both magnification and resolution to image polymer architecture in the <10 nm range. For the first time, polymer structure has been detailed that clearly displays molecularly imprinted cavities, ranging from 5-50 nm in size, that correlate (in terms of size) with the protein molecule employed as the imprinting template. The modified critical point drying sample preparation technique used may potentially play a key role in the imaging of all molecularly imprinted polymers, particularly those prepared in the aqueous phase

From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments

Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae)

Self-incompatibility (SI) is widespread in the angiosperms, but identifying the biochemical components of SI mechanisms has proven to be difficult in most lineages. Coffea (coffee; Rubiaceae) is a genus of old-world tropical understory trees in which the vast majority of diploid species utilize a mechanism of gametophytic self-incompatibility (GSI). The S-RNase GSI system was one of the first SI mechanisms to be biochemically characterized, and likely represents the ancestral Eudicot condition as evidenced by its functional characterization in both asterid (Solanaceae, Plantaginaceae) and rosid (Rosaceae) lineages. The S-RNase GSI mechanism employs the activity of class III RNase T2 proteins to terminate the growth of "self" pollen tubes. Here, we investigate the mechanism of Coffea GSI and specifically examine the potential for homology to S-RNase GSI by sequencing class III RNase T2 genes in populations of 14 African and Madagascan Coffea species and the closely related self-compatible species Psilanthus ebracteolatus. Phylogenetic analyses of these sequences aligned to a diverse sample of plant RNase T2 genes show that the Coffea genome contains at least three class III RNase T2 genes. Patterns of tissue-specific gene expression identify one of these RNase T2 genes as the putative Coffea S-RNase gene. We show that populations of SI Coffea are remarkably polymorphic for putative S-RNase alleles, and exhibit a persistent pattern of trans-specific polymorphism characteristic of all S-RNase genes previously isolated from GSI Eudicot lineages. We thus conclude that Coffea GSI is most likely homologous to the classic Eudicot S-RNase system, which was retained since the divergence of the Rubiaceae lineage from an ancient SI Eudicot ancestor, nearly 90 million years ago.United States National Science Foundation [0849186]; Society of Systematic Biologists; American Society of Plant Taxonomists; Duke University Graduate Schoolinfo:eu-repo/semantics/publishedVersio

Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives

Reverse Engineering a Signaling Network Using Alternative Inputs

One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an “AIs-Deletions matrix” that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams

Multi-drug resistant Acinetobacter infections in critically injured Canadian forces soldiers

<p>Abstract</p> <p>Background</p> <p>Military members, injured in Afghanistan or Iraq, have returned home with multi-drug resistant <it>Acinetobacter baumannii </it>infections. The source of these infections is unknown.</p> <p>Methods</p> <p>Retrospective study of all Canadian soldiers who were injured in Afghanistan and who required mechanical ventilation from January 1 2006 to September 1 2006. Patients who developed <it>A. baumannii </it>ventilator associated pneumonia (VAP) were identified. All <it>A. baumannii </it>isolates were retrieved for study patients and compared with <it>A. baumannii </it>isolates from environmental sources from the Kandahar military hospital using pulsed-field gel electrophoresis (PFGE).</p> <p>Results</p> <p>During the study period, six Canadian Forces (CF) soldiers were injured in Afghanistan, required mechanical ventilation and were repatriated to Canadian hospitals. Four of these patients developed <it>A. baumannii </it>VAP. <it>A. baumannii </it>was also isolated from one environmental source in Kandahar – a ventilator air intake filter. Patient isolates were genetically indistinguishable from each other and from the isolates cultured from the ventilator filter. These isolates were resistant to numerous classes of antimicrobials including the carbapenems.</p> <p>Conclusion</p> <p>These results suggest that the source of <it>A. baumannii </it>infection for these four patients was an environmental source in the military field hospital in Kandahar. A causal linkage, however, was not established with the ventilator. This study suggests that infection control efforts and further research should be focused on the military field hospital environment to prevent further multi-drug resistant <it>A. baumannii </it>infections in injured soldiers.</p

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Selected static foot assessments do not predict medial longitudinal arch motion during running

Background: Static assessments of the foot are commonly advocated within the running community to classify the foot with a view to recommending the appropriate type of running shoe. The aim of this work was to determine whether selected static foot assessment could predict medial longitudinal arch (MLA) motion during running. Methods: Fifteen physically active males (27 ± 5 years, 1.77 ± 0.04m, 80 ± 10kg) participated in the study. Foot Posture Index (FPI-6), MLA angle and rearfoot angle were measured in a relaxed standing position. MLA motion was calculated using the position of retro-reflective markers tracked by a VICON motion analysis system, while participants ran barefoot on a treadmill at a self-selected pace (2.8 ± 0.5m.s-1). Bivariate linear regression was used to determine whether the static measures predicted MLA deformation and MLA angles at initial contact, midsupport and toe off. Results: All three foot classification measures were significant predictors of MLA angle at initial contact, midsupport and toe off (p < .05) explaining 41-90% of the variance. None of the static foot classification measures were significant predictors of MLA deformation during the stance phase of running. Conclusion: Selected static foot measures did not predict dynamic MLA deformation during running. Given that MLA deformation has theoretically been linked to running injuries, the clinical relevance of predicting MLA angle at discrete time points during the stance phase of running is questioned. These findings also question the validity of the selected static foot classification measures when looking to characterise the foot during running. This indicates that alternative means of assessing the foot to inform footwear selection are required

Reduction of Dopamine Level Enhances the Attractiveness of Male Drosophila to Other Males

Dopamine is an important neuromodulator in animals and its roles in mammalian sexual behavior are extensively studied. Drosophila as a useful model system is widely used in many fields of biological studies. It has been reported that dopamine reduction can affect female receptivity in Drosophila and leave male-female courtship behavior unaffected. Here, we used genetic and pharmacological approaches to decrease the dopamine level in dopaminergic cells in Drosophila, and investigated the consequence of this manipulation on male homosexual courtship behavior. We find that reduction of dopamine level can induce Drosophila male-male courtship behavior, and that this behavior is mainly due to the increased male attractiveness or decreased aversiveness towards other males, but not to their enhanced propensity to court other males. Chemical signal input probably plays a crucial role in the male-male courtship induced by the courtees with reduction of dopamine. Our finding provides insight into the relationship between the dopamine reduction and male-male courtship behavior, and hints dopamine level is important for controlling Drosophila courtship behavior

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets