83 research outputs found

    A Day in the Life of Microcystis aeruginosa Strain PCC 7806 as Revealed by a Transcriptomic Analysis

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    The cyanobacterium, Microcystis aeruginosa, is able to proliferate in a wide range of freshwater ecosystems and to produce many secondary metabolites that are a threat to human and animal health. The dynamic of this production and more globally the metabolism of this species is still poorly known. A DNA microarray based on the genome of M. aeruginosa PCC 7806 was constructed and used to study the dynamics of gene expression in this cyanobacterium during the light/dark cycle, because light is a critical factor for this species, like for other photosynthetic microorganisms. This first application of transcriptomics to a Microcystis species has revealed that more than 25% of the genes displayed significant changes in their transcript abundance during the light/dark cycle and in particular during the dark/light transition. The metabolism of M. aeruginosa is compartmentalized between the light period, during which carbon uptake, photosynthesis and the reductive pentose phosphate pathway lead to the synthesis of glycogen, and the dark period, during which glycogen degradation, the oxidative pentose phosphate pathway, the TCA branched pathway and ammonium uptake promote amino acid biosynthesis. We also show that the biosynthesis of secondary metabolites, such as microcystins, aeruginosin and cyanopeptolin, occur essentially during the light period, suggesting that these metabolites may interact with the diurnal part of the central metabolism

    Patient-derived glioblastoma cells show significant heterogeneity in treatment responses to the inhibitor-of-apoptosis-protein antagonist birinapant.

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    BACKGROUND: Resistance to temozolomide (TMZ) greatly limits chemotherapeutic effectiveness in glioblastoma (GBM). Here we analysed the ability of the Inhibitor-of-apoptosis-protein (IAP) antagonist birinapant to enhance treatment responses to TMZ in both commercially available and patient-derived GBM cells. METHODS: Responses to TMZ and birinapant were analysed in a panel of commercial and patient-derived GBM cell lines using colorimetric viability assays, flow cytometry, morphological analysis and protein expression profiling of pro- and antiapoptotic proteins. Responses in vivo were analysed in an orthotopic xenograft GBM model. RESULTS: Single-agent treatment experiments categorised GBM cells into TMZ-sensitive cells, birinapant-sensitive cells, and cells that were insensitive to either treatment. Combination treatment allowed sensitisation to therapy in only a subset of resistant GBM cells. Cell death analysis identified three principal response patterns: Type A cells that readily activated caspase-8 and cell death in response to TMZ while addition of birinapant further sensitised the cells to TMZ-induced cell death; Type B cells that readily activated caspase-8 and cell death in response to birinapant but did not show further sensitisation with TMZ; and Type C cells that showed no significant cell death or moderately enhanced cell death in the combined treatment paradigm. Furthermore, in vivo, a Type C patient-derived cell line that was TMZ-insensitive in vitro and showed a strong sensitivity to TMZ and TMZ plus birinapant treatments. CONCLUSIONS: Our results demonstrate remarkable differences in responses of patient-derived GBM cells to birinapant single and combination treatments, and suggest that therapeutic responses in vivo may be greatly affected by the tumour microenvironment

    A Scalable System for Production of Functional Pancreatic Progenitors from Human Embryonic Stem Cells

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    Development of a human embryonic stem cell (hESC)-based therapy for type 1 diabetes will require the translation of proof-of-principle concepts into a scalable, controlled, and regulated cell manufacturing process. We have previously demonstrated that hESC can be directed to differentiate into pancreatic progenitors that mature into functional glucose-responsive, insulin-secreting cells in vivo. In this study we describe hESC expansion and banking methods and a suspension-based differentiation system, which together underpin an integrated scalable manufacturing process for producing pancreatic progenitors. This system has been optimized for the CyT49 cell line. Accordingly, qualified large-scale single-cell master and working cGMP cell banks of CyT49 have been generated to provide a virtually unlimited starting resource for manufacturing. Upon thaw from these banks, we expanded CyT49 for two weeks in an adherent culture format that achieves 50–100 fold expansion per week. Undifferentiated CyT49 were then aggregated into clusters in dynamic rotational suspension culture, followed by differentiation en masse for two weeks with a four-stage protocol. Numerous scaled differentiation runs generated reproducible and defined population compositions highly enriched for pancreatic cell lineages, as shown by examining mRNA expression at each stage of differentiation and flow cytometry of the final population. Islet-like tissue containing glucose-responsive, insulin-secreting cells was generated upon implantation into mice. By four- to five-months post-engraftment, mature neo-pancreatic tissue was sufficient to protect against streptozotocin (STZ)-induced hyperglycemia. In summary, we have developed a tractable manufacturing process for the generation of functional pancreatic progenitors from hESC on a scale amenable to clinical entry

    Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

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    New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era

    In Silico Whole Genome Association Scan for Murine Prepulse Inhibition

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    Background The complex trait of prepulse inhibition (PPI) is a sensory gating measure related to schizophrenia and can be measured in mice. Large-scale public repositories of inbred mouse strain genotypes and phenotypes such as PPI can be used to detect Quantitative Trait Loci (QTLs) in silico. However, the method has been criticized for issues including insufficient number of strains, not controlling for false discoveries, the complex haplotype structure of inbred mice, and failing to account for genotypic and phenotypic subgroups. Methodology/Principal Findings We have implemented a method that addresses these issues by incorporating phylogenetic analyses, multilevel regression with mixed effects, and false discovery rate (FDR) control. A genome-wide scan for PPI was conducted using over 17,000 single nucleotide polymorphisms (SNPs) in 37 strains phenotyped. Eighty-nine SNPs were significant at a false discovery rate (FDR) of 5%. After accounting for long-range linkage disequilibrium, we found 3 independent QTLs located on murine chromosomes 1 and 13. One of the PPI positives corresponds to a region of human chromosome 6p which includes DTNBP1, a gene implicated in schizophrenia. Another region includes the gene Tsn which alters PPI when knocked out. These genes also appear to have correlated expression with PPI. Conclusions/Significance These results support the usefulness of using an improved in silico mapping method to identify QTLs for complex traits such as PPI which can be then be used for to help identify loci influencing schizophrenia in humans

    Mapping the Conformational Dynamics and Pathways of Spontaneous Steric Zipper Peptide Oligomerization

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    The process of protein misfolding and self-assembly into various, polymorphic aggregates is associated with a number of important neurodegenerative diseases. Only recently, crystal structures of several short peptides have provided detailed structural insights into -sheet rich aggregates, known as amyloid fibrils. Knowledge about early events of the formation and interconversion of small oligomeric states, an inevitable step in the cascade of peptide self-assembly, however, remains still limited

    Differential effects of prenatal and postnatal expressions of mutant human DISC1 on neurobehavioral phenotypes in transgenic mice: evidence for neurodevelopmental origin of major psychiatric disorders

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    Strong genetic evidence implicates mutations and polymorphisms in the gene Disrupted-In-Schizophrenia-1 (DISC1) as risk factors for both schizophrenia and mood disorders. Recent studies have shown that DISC1 has important functions in both brain development and adult brain function. We have described earlier a transgenic mouse model of inducible expression of mutant human DISC1 (hDISC1) that acts in a dominant-negative manner to induce the marked neurobehavioral abnormalities. To gain insight into the roles of DISC1 at various stages of neurodevelopment, we examined the effects of mutant hDISC1 expressed during (1) only prenatal period, (2) only postnatal period, or (3) both periods. All periods of expression similarly led to decreased levels of cortical dopamine (DA) and fewer parvalbumin-positive neurons in the cortex. Combined prenatal and postnatal expression produced increased aggression and enhanced response to psychostimulants in male mice along with increased linear density of dendritic spines on neurons of the dentate gyrus of the hippocampus, and lower levels of endogenous DISC1 and LIS1. Prenatal expression only resulted in smaller brain volume, whereas selective postnatal expression gave rise to decreased social behavior in male mice and depression-like responses in female mice as well as enlarged lateral ventricles and decreased DA content in the hippocampus of female mice, and decreased level of endogenous DISC1. Our data show that mutant hDISC1 exerts differential effects on neurobehavioral phenotypes, depending on the stage of development at which the protein is expressed. The multiple and diverse abnormalities detected in mutant DISC1 mice are reminiscent of findings in major mental diseases

    Advancing schizophrenia drug discovery : optimizing rodent models to bridge the translational gap

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    Although our knowledge of the pathophysiology of schizophrenia has increased, treatments for this devastating illness remain inadequate. Here, we critically assess rodent models and behavioural end points used in schizophrenia drug discovery and discuss why these have not led to improved treatments. We provide a perspective on how new models, based on recent advances in the understanding of the genetics and neural circuitry underlying schizophrenia, can bridge the translational gap and lead to the development of more effective drugs. We conclude that previous serendipitous approaches should be replaced with rational strategies for drug discovery in integrated preclinical and clinical programmes. Validation of drug targets in disease-based models that are integrated with translationally relevant end point assessments will reduce the current attrition rate in schizophrenia drug discovery and ultimately lead to therapies that tackle the disease process
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