Identification and functional analysis of vibrio vulnificus SmcR, a novel global regulator

Recently, quorum sensing has been implicated as an important global regulator controlling the production of numerous virulence factors such as capsular polysaccharides in bacterial pathogens. The nucleotide and deduced amino acid sequences of smcR, a homolog of V. harveyi luxR identified from V. vulnificus ATCC29307, were analyzed. The amino acid sequence of SmcR from V. vulnificus was 72 to 92% similar to those of LuxR homologs from Vibrio spp. Functions of SmcR were assessed by the construction of an isogenic mutant, whose smcR gene was inactivated by allelic exchanges, and by evaluating its phenotype changes in vitro and in mice. The disruption of smcR resulted in a significant alteration in biofilm formation, in type of colony morphology, and in motility. When compared with the wild-type, the smcR mutant exhibited reduced survival under adverse conditions, such as acidic pH and hyperosmotic stress. The smcR mutant exhibited decreased cytotoxic activity toward INT 407 cells in vitro. Furthermore, the intraperitoneal LD50 of the smcR mutant was approximately 10(2) times higher than that of parental wild-type. Therefore, it appears that SmcR is a novel global regulator, controlling numerous genes contributing to the pathogenesis as well as survival of V. vulnificus.     

Hyperglycemia and hyperinsulinemia have additive effects on activation and proliferation of pancreatic stellate cells: possible explanation of islet-specific fibrosis in type 2 diabetes mellitus

Pancreatic islet fibrosis observed in Type 2 diabetes is one of the major factors leading to progressive beta-cell loss and dysfunction. Despite its importance, the mechanism of islet-restricted fibrogenesis associated with pancreatic stellate cell (PSC) activation and proliferation remains to be defined. Therefore, we studied whether the islet-specific environment represented by hyperglycemia and hyperinsulinemia had additive effects on the activation and proliferation of cultured rat PSCs. Cells were stimulated to activate and proliferate with glucose and insulin, either individually or concomitantly. Both stimuli promoted PSC proliferation and extracellular signal-regulated kinase (ERK) 1/2 phosphorylation independently, but an additive effect was also demonstrated. Blockade of ERK signaling by the mitogen-activated protein kinase kinase (MEK) inhibitor, U0126, suppressed both glucose- and insulin-induced ERK 1/2 phosphorylation and PSC proliferation. Glucose and insulin-induced ERK 1/2 phosphorylation also stimulated connective tissue growth factor gene expression. Thus, hyperglycemia and hyperinsulinemia are two crucial mitogenic factors that activate and proliferate PSCs, and the presence of both states will amplify this response.     

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.     

Large seasonal variation in phytoplankton production in the Amundsen Sea

Polar Biology - To better estimate annual primary production in the Amundsen Sea, which is one of the highest productivity regions in the Southern Ocean, the seasonal variations in carbon and...     

Synthesis and evaluation of cytotoxic effects of hanultarin and its derivatives

One of the known cytotoxic lignans is (-)-1-O-feruloyl-secoisolariciresinol designated as hanultarin, which was isolated from the seeds of Trichosanthes kirilowii. In this Letter, we described the first synthesis of 1-O-feruloyl-secoisolariciresinol, 1,4-O-diferuloyl-secoisolariceresinol and their analogues. The cytotoxicities of these compounds were evaluated against several cancer cell lines. Interestingly, we found that the feruloyl diester derivative of secoisolariciresinol was the most active cytotoxic compound against all the cancer cells tested in this experiment. The IC(50) values of the1,4-O-diferuloyl-secoisolariceresinol were in the range of 7.1-9.8μM except one cell line. In considering that both ferulic acid and secoisolariciresinol are commonly found in many plants and have no cytotoxicity, this finding is remarkable in that simple covalent bonds between the ferulic acid and secoisolariciresinol cause a cytotoxic effect.     

Unraveling the biological roles of reactive oxygen species

Reactive oxygen species are not only harmful agents that cause oxidative damage in pathologies, they also have important roles as regulatory agents in a range of biological phenomena. The relatively recent development of this more nuanced view presents a challenge to the biomedical research community on how best to assess the significance of reactive oxygen species and oxidative damage in biological systems. Considerable progress is being made in addressing these issues, and here we survey some recent developments for those contemplating research in this area.