51 research outputs found

    Suppression of acute and chronic inflammation by orally administered prostaglandins

    Full text link
    Oral administration of a stable analog of prostaglandin E, (PGE 1 ), 15-(S)-15-methyl-prostaglandin E 1 , can suppress both chronic adjuvant-induced polyarthritis and acute immune complex-induced vasculitis in a dose dependent manner. Histopathologic studies of tibiotarsal joints from rats with adjuvant disease showed suppression of arthritis in animals treated with the PGE, analog from time of adjuvant challenge. This study represents the first demonstration of suppressed experimental polyarthritis by an orally administered prostaglandin. Suppression of the acute immune complex-induced vasculitis was demonstrated using 15-methyl-PGE, administered orally 12 hours prior to antigen-antibody challenge. Diminution of tissue injury resulting from immune complex-induced vasculitis is reflected by a decrease in vaso-permeability, indicating suppressed vascular damage in animals treated with prostaglandin. These studies demonstrate the potential use of orally active prostaglandins as an antiinflammatory agent.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/37757/1/1780240906_ftp.pd

    An Acidic Motif Retains Vesicular Monoamine Transporter 2 on Large Dense Core Vesicles

    Get PDF
    The release of biogenic amines from large dense core vesicles (LDCVs) depends on localization of the vesicular monoamine transporter VMAT2 to LDCVs. We now find that a cluster of acidic residues including two serines phosphorylated by casein kinase 2 is required for the localization of VMAT2 to LDCVs. Deletion of the acidic cluster promotes the removal of VMAT2 from LDCVs during their maturation. The motif thus acts as a signal for retention on LDCVs. In addition, replacement of the serines by glutamate to mimic phosphorylation promotes the removal of VMAT2 from LDCVs, whereas replacement by alanine to prevent phosphorylation decreases removal. Phosphorylation of the acidic cluster thus appears to reduce the localization of VMAT2 to LDCVs by inactivating a retention mechanism

    Modeling Evolutionary Dynamics of Epigenetic Mutations in Hierarchically Organized Tumors

    Get PDF
    The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult to interpret. An important reason for this is that most experimental data that support this model rely on transplantation studies. In this study we use a novel cellular Potts model to elucidate the dynamics of established malignancies that are driven by a small subset of CSCs. Our results demonstrate that epigenetic mutations that occur during mitosis display highly altered dynamics in CSC-driven malignancies compared to a classical, non-hierarchical model of growth. In particular, the heterogeneity observed in CSC-driven tumors is considerably higher. We speculate that this feature could be used in combination with epigenetic (methylation) sequencing studies of human malignancies to prove or refute the CSC hypothesis in established tumors without the need for transplantation. Moreover our tumor growth simulations indicate that CSC-driven tumors display evolutionary features that can be considered beneficial during tumor progression. Besides an increased heterogeneity they also exhibit properties that allow the escape of clones from local fitness peaks. This leads to more aggressive phenotypes in the long run and makes the neoplasm more adaptable to stringent selective forces such as cancer treatment. Indeed when therapy is applied the clone landscape of the regrown tumor is more aggressive with respect to the primary tumor, whereas the classical model demonstrated similar patterns before and after therapy. Understanding these often counter-intuitive fundamental properties of (non-)hierarchically organized malignancies is a crucial step in validating the CSC concept as well as providing insight into the therapeutical consequences of this model

    Power, Food and Agriculture: Implications for Farmers, Consumers and Communities

    Full text link

    Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    Get PDF
    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison
    corecore