Inhibition of cholesterol esterases by Δ1-tetrahydrocannabinol

Δ¹-Tetrahydrocannabinol was found to inhibit the action of esterases derived from rat adrenal and luteinized ovary on exogenous cholesteryl palmitate. The drug was effective at a dose of 3.2μM causing greater than 30% inhibition; at 16μM almost complete inhibition occured. These findings are similar to those we have recently reported with mouse Leydig cells (1) showing that this is an effect common to steroidogenic tissues and raising the possibility that a variety of endocrine effects of this drug may be due to direct action on these tissues.     

Computed tomography and automated image analysis of prehistoric femora

Non-invasive characterization of limb bone cross-sectional geometry would be useful for biomechanical analyses of skeletal collections. Computed tomography (CT) is potentially the method of choice. Additionally, CT images are suitable for automated analysis. CT is here shown to be both accurate and precise in the analysis of cross-sectional geometry of prehistoric femora. Beam hardening artifacts can be reduced by using a water bath. As the availability of CT for research increases, both bone density and geometry could be determined simultaneously with this method.     

Prostaglandin biosynthesis and stimulation of cyclic AMP in primary monolayer cultures of epithelial cells from mouse mammary gland

Cyclic AMP levels in primary monolayer cultures of epithelial cells prepared from mid-pregnant mice are stimulated by prostaglandin E₁ and E₂. Prostaglandin F_1α and F_2α have only a slight effect upon cyclic AMP levels. In the absence of phosphodiesterase inhibitors the rise in cyclic AMP produced by PGE₁ is only transient and the levels return to normal within 30 minutes. High concentrations (16 mM) of theophylline are needed to prevent this decline, suggesting that the phosphodiesterase activity of epithelial cells in culture is high. However, theophylline alone produced only a small increase in basal cyclic AMP levels even over a 2-hour period indicating that basal cyclic AMP is turned over more slowly than cyclic AMP produced in response to stimulation with PGE₁.Both PGE and PGF synthesis were monitored using radioimmunoassay procedures previously reported. The observed levels were found to decrease as cell density increased and were sensitive to the addition of agents such as collagen and naproxen.     

Expression, Purification, and Initial Characterization of the Recombinant Storage Protein Precursor ofTheobroma cacao

The gene encoding the 67-kDa cocoa storage protein precursor has been cloned fromTheobroma cacaoand expressed inEscherichia coliusing the pET expression system. The recombinant storage protein has been renatured from inclusion bodies at 30°C using 20 m glycine–NaOH buffer, pH 10.0, containing 1 m oxidized glutathione and 0.1% Brij. The renatured protein was purified and demonstrated to adopt a stable native conformation by optical spectroscopy. Secondary structure analysis from circular dichroism indicated the protein to be 23% α-helix and 38% β-sheet, in close agreement with values obtained using a secondary structure prediction program.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Viral Co-Infections in Pediatric Patients Hospitalized with Lower Tract Acute Respiratory Infections

Background

Molecular techniques can often reveal a broader range of pathogens in respiratory infections. We aim to investigate the prevalence and age pattern of viral co-infection in children hospitalized with lower tract acute respiratory infection (LT-ARI), using molecular techniques.

Methods

A nested polymerase chain reaction approach was used to detect Influenza (A, B), metapneumovirus, respiratory syncytial virus (RSV), parainfluenza (1–4), rhinovirus, adenovirus (A—F), bocavirus and coronaviruses (NL63, 229E, OC43) in respiratory samples of children with acute respiratory infection prospectively admitted to any of the GENDRES network hospitals between 2011–2013. The results were corroborated in an independent cohort collected in the UK.

Results

A total of 204 and 97 nasopharyngeal samples were collected in the GENDRES and UK cohorts, respectively. In both cohorts, RSV was the most frequent pathogen (52.9% and 36.1% of the cohorts, respectively). Co-infection with multiple viruses was found in 92 samples (45.1%) and 29 samples (29.9%), respectively; this was most frequent in the 12–24 months age group. The most frequently observed co-infection patterns were RSV—Rhinovirus (23 patients, 11.3%, GENDRES cohort) and RSV—bocavirus / bocavirus—influenza (5 patients, 5.2%, UK cohort).

Conclusion

The presence of more than one virus in pediatric patients admitted to hospital with LT-ARI is very frequent and seems to peak at 12–24 months of age. The clinical significance of these findings is unclear but should warrant further analysis.     

Plant neighbor identity influences plant biochemistry and physiology related to defense

Background  

Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, Centaurea maculosa, would modulate their defensive strategy in response to different plant neighbors.     

Human medial frontal cortex mediates unconscious inhibition of voluntary action

Within the medial frontal cortex, the supplementary eye field (SEF), supplementary motor area (SMA), and pre-SMA have been implicated in the control of voluntary action, especially during motor sequences or tasks involving rapid choices between competing response plans. However, the precise roles of these areas remain controversial. Here, we study two extremely rare patients with microlesions of the SEF and SMA to demonstrate that these areas are critically involved in unconscious and involuntary motor control. We employed masked-prime stimuli that evoked automatic inhibition in healthy people and control patients with lateral premotor or pre-SMA damage. In contrast, our SEF/SMA patients showed a complete reversal of the normal inhibitory effect--ocular or manual--corresponding to the functional subregion lesioned. These findings imply that the SEF and SMA mediate automatic effector-specific suppression of motor plans. This automatic mechanism may contribute to the participation of these areas in the voluntary control of action.     

Glutathione and Gts1p drive beneficial variability in the cadmium resistances of individual yeast cells

Phenotypic heterogeneity among individual cells within isogenic populations is widely documented, but its consequences are not well understood. Here, cell-to-cell variation in the stress resistance of Saccharomyces cerevisiae, particularly to cadmium, was revealed to depend on the antioxidant glutathione. Heterogeneity was decreased strikingly in gsh1 mutants. Furthermore, cells sorted according to differing reduced-glutathione (GSH) contents exhibited differing stress resistances. The vacuolar GSH-conjugate pathway of detoxification was implicated in heterogeneous Cd resistance. Metabolic oscillations (ultradian rhythms) in yeast are known to modulate single-cell redox and GSH status. Gts1p stabilizes these oscillations and was found to be required for heterogeneous Cd and hydrogen-peroxide resistance, through the same pathway as Gsh1p. Expression of GTS1 from a constitutive tet-regulated promoter suppressed oscillations and heterogeneity in GSH content, and resulted in decreased variation in stress resistance. This enabled manipulation of the degree of gene expression noise in cultures. It was shown that cells expressing Gts1p heterogeneously had a competitive advantage over more-homogeneous cell populations (with the same mean Gts1p expression), under continuous and fluctuating stress conditions. The results establish a novel molecular mechanism for single-cell heterogeneity, and demonstrate experimentally fitness advantages that depend on deterministic variation in gene expression within cell populations.