PIP5KIβ Selectively Modulates Apical Endocytosis in Polarized Renal Epithelial Cells

Localized synthesis of phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] at clathrin coated pits (CCPs) is crucial for the recruitment of adaptors and other components of the internalization machinery, as well as for regulating actin dynamics during endocytosis. PtdIns(4,5)P2 is synthesized from phosphatidylinositol 4-phosphate by any of three phosphatidylinositol 5-kinase type I (PIP5KI) isoforms (α, β or γ). PIP5KIβ localizes almost exclusively to the apical surface in polarized mouse cortical collecting duct cells, whereas the other isoforms have a less polarized membrane distribution. We therefore investigated the role of PIP5KI isoforms in endocytosis at the apical and basolateral domains. Endocytosis at the apical surface is known to occur more slowly than at the basolateral surface. Apical endocytosis was selectively stimulated by overexpression of PIP5KIβ whereas the other isoforms had no effect on either apical or basolateral internalization. We found no difference in the affinity for PtdIns(4,5)P2-containing liposomes of the PtdIns(4,5)P2 binding domains of epsin and Dab2, consistent with a generic effect of elevated PtdIns(4,5)P2 on apical endocytosis. Additionally, using apical total internal reflection fluorescence imaging and electron microscopy we found that cells overexpressing PIP5KIβ have fewer apical CCPs but more internalized coated structures than control cells, consistent with enhanced maturation of apical CCPs. Together, our results suggest that synthesis of PtdIns(4,5)P2 mediated by PIP5KIβ is rate limiting for apical but not basolateral endocytosis in polarized kidney cells. PtdIns(4,5)P2 may be required to overcome specific structural constraints that limit the efficiency of apical endocytosis. © 2013 Szalinski et al

Expanding the clinical phenotype of IARS2-related mitochondrial disease.

BACKGROUND: IARS2 encodes a mitochondrial isoleucyl-tRNA synthetase, a highly conserved nuclear-encoded enzyme required for the charging of tRNAs with their cognate amino acid for translation. Recently, pathogenic IARS2 variants have been identified in a number of patients presenting broad clinical phenotypes with autosomal recessive inheritance. These phenotypes range from Leigh and West syndrome to a new syndrome abbreviated CAGSSS that is characterised by cataracts, growth hormone deficiency, sensory neuropathy, sensorineural hearing loss, and skeletal dysplasia, as well as cataract with no additional anomalies. METHODS: Genomic DNA from Iranian probands from two families with consanguineous parental background and overlapping CAGSSS features were subjected to exome sequencing and bioinformatics analysis. RESULTS: Exome sequencing and data analysis revealed a novel homozygous missense variant (c.2625C > T, p.Pro909Ser, NM_018060.3) within a 14.3 Mb run of homozygosity in proband 1 and a novel homozygous missense variant (c.2282A > G, p.His761Arg) residing in an ~ 8 Mb region of homozygosity in a proband of the second family. Patient-derived fibroblasts from proband 1 showed normal respiratory chain enzyme activity, as well as unchanged oxidative phosphorylation protein subunits and IARS2 levels. Homology modelling of the known and novel amino acid residue substitutions in IARS2 provided insight into the possible consequence of these variants on function and structure of the protein. CONCLUSIONS: This study further expands the phenotypic spectrum of IARS2 pathogenic variants to include two patients (patients 2 and 3) with cataract and skeletal dysplasia and no other features of CAGSSS to the possible presentation of the defects in IARS2. Additionally, this study suggests that adult patients with CAGSSS may manifest central adrenal insufficiency and type II esophageal achalasia and proposes that a variable sensorineural hearing loss onset, proportionate short stature, polyneuropathy, and mild dysmorphic features are possible, as seen in patient 1. Our findings support that even though biallelic IARS2 pathogenic variants can result in a distinctive, clinically recognisable phenotype in humans, it can also show a wide range of clinical presentation from severe pediatric neurological disorders of Leigh and West syndrome to both non-syndromic cataract and cataract accompanied by skeletal dysplasia

History of clinical transplantation

The emergence of transplantation has seen the development of increasingly potent immunosuppressive agents, progressively better methods of tissue and organ preservation, refinements in histocompatibility matching, and numerous innovations is surgical techniques. Such efforts in combination ultimately made it possible to successfully engraft all of the organs and bone marrow cells in humans. At a more fundamental level, however, the transplantation enterprise hinged on two seminal turning points. The first was the recognition by Billingham, Brent, and Medawar in 1953 that it was possible to induce chimerism-associated neonatal tolerance deliberately. This discovery escalated over the next 15 years to the first successful bone marrow transplantations in humans in 1968. The second turning point was the demonstration during the early 1960s that canine and human organ allografts could self-induce tolerance with the aid of immunosuppression. By the end of 1962, however, it had been incorrectly concluded that turning points one and two involved different immune mechanisms. The error was not corrected until well into the 1990s. In this historical account, the vast literature that sprang up during the intervening 30 years has been summarized. Although admirably documenting empiric progress in clinical transplantation, its failure to explain organ allograft acceptance predestined organ recipients to lifetime immunosuppression and precluded fundamental changes in the treatment policies. After it was discovered in 1992 that long-surviving organ transplant recipient had persistent microchimerism, it was possible to see the mechanistic commonality of organ and bone marrow transplantation. A clarifying central principle of immunology could then be synthesized with which to guide efforts to induce tolerance systematically to human tissues and perhaps ultimately to xenografts

The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites

Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of biological neural processing. It is not yet well understood which processing capabilities require detailed branching structures. Therefore, we analyzed the processing capabilities of full or partially branched reduced models. These models were created by collapsing the dendritic tree of a full morphological model of a globus pallidus (GP) neuron while preserving its total surface area and electrotonic length, as well as its passive and active parameters. Dendritic trees were either collapsed into single cables (unbranched models) or the full complement of branch points was preserved (branched models). Both reduction strategies allowed us to compare dynamics between all models using the same channel density settings. Full model responses to somatic inputs were generally preserved by both types of reduced model while dendritic input responses could be more closely preserved by branched than unbranched reduced models. However, features strongly influenced by local dendritic input resistance, such as active dendritic sodium spike generation and propagation, could not be accurately reproduced by any reduced model. Based on our analyses, we suggest that there are intrinsic differences in processing capabilities between unbranched and branched models. We also indicate suitable applications for different levels of reduction, including fast searches of full model parameter space

A Computational Investigation on the Connection between Dynamics Properties of Ribosomal Proteins and Ribosome Assembly

Assembly of the ribosome from its protein and RNA constituents has been studied extensively over the past 50 years, and experimental evidence suggests that prokaryotic ribosomal proteins undergo conformational changes during assembly. However, to date, no studies have attempted to elucidate these conformational changes. The present work utilizes computational methods to analyze protein dynamics and to investigate the linkage between dynamics and binding of these proteins during the assembly of the ribosome. Ribosomal proteins are known to be positively charged and we find the percentage of positive residues in r-proteins to be about twice that of the average protein: Lys+Arg is 18.7% for E. coli and 21.2% for T. thermophilus. Also, positive residues constitute a large proportion of RNA contacting residues: 39% for E. coli and 46% for T. thermophilus. This affirms the known importance of charge-charge interactions in the assembly of the ribosome. We studied the dynamics of three primary proteins from E. coli and T. thermophilus 30S subunits that bind early in the assembly (S15, S17, and S20) with atomic molecular dynamic simulations, followed by a study of all r-proteins using elastic network models. Molecular dynamics simulations show that solvent-exposed proteins (S15 and S17) tend to adopt more stable solution conformations than an RNA-embedded protein (S20). We also find protein residues that contact the 16S rRNA are generally more mobile in comparison with the other residues. This is because there is a larger proportion of contacting residues located in flexible loop regions. By the use of elastic network models, which are computationally more efficient, we show that this trend holds for most of the 30S r-proteins

Francisella tularensis Uses Cholesterol and Clathrin-Based Endocytic Mechanisms to Invade Hepatocytes

Francisella tularensis are highly infectious microbes that cause the disease tularemia. Although much of the bacterial burden is carried in non-phagocytic cells, the strategies these pathogens use to invade these cells remains elusive. To examine these mechanisms we developed two in vitro Francisella-based infection models that recapitulate the non-phagocytic cell infections seen in livers of infected mice. Using these models we found that Francisella novicida exploit clathrin and cholesterol dependent mechanisms to gain entry into hepatocytes. We also found that the clathrin accessory proteins AP-2 and Eps15 co-localized with invading Francisella novicida as well as the Francisella Live Vaccine Strain (LVS) during hepatocyte infections. Interestingly, caveolin, a protein involved in the invasion of Francisella in phagocytic cells, was not required for non-phagocytic cell infections. These results demonstrate a novel endocytic mechanism adopted by Francisella and highlight the divergence in strategies these pathogens utilize between non-phagocytic and phagocytic cell invasion

Targeting of human interleukin-12B by small hairpin RNAs in xenografted psoriatic skin

<p>Abstract</p> <p>Background</p> <p>Psoriasis is a chronic inflammatory skin disorder that shows as erythematous and scaly lesions. The pathogenesis of psoriasis is driven by a dysregulation of the immune system which leads to an altered cytokine production. Proinflammatory cytokines that are up-regulated in psoriasis include tumor necrosis factor alpha (TNFα), interleukin-12 (IL-12), and IL-23 for which monoclonal antibodies have already been approved for clinical use. We have previously documented the therapeutic applicability of targeting TNFα mRNA for RNA interference-mediated down-regulation by anti-TNFα small hairpin RNAs (shRNAs) delivered by lentiviral vectors to xenografted psoriatic skin. The present report aims at targeting mRNA encoding the shared p40 subunit (IL-12B) of IL-12 and IL-23 by cellular transduction with lentiviral vectors encoding anti-IL12B shRNAs.</p> <p>Methods</p> <p>Effective anti-IL12B shRNAs are identified among a panel of shRNAs by potency measurements in cultured cells. The efficiency and persistency of lentiviral gene delivery to xenografted human skin are investigated by bioluminescence analysis of skin treated with lentiviral vectors encoding the luciferase gene. shRNA-expressing lentiviral vectors are intradermally injected in xenografted psoriatic skin and the effects of the treatment evaluated by clinical psoriasis scoring, by measurements of epidermal thickness, and IL-12B mRNA levels.</p> <p>Results</p> <p>Potent and persistent transgene expression following a single intradermal injection of lentiviral vectors in xenografted human skin is reported. Stable IL-12B mRNA knockdown and reduced epidermal thickness are achieved three weeks after treatment of xenografted psoriatic skin with lentivirus-encoded anti-IL12B shRNAs. These findings mimick the results obtained with anti-TNFα shRNAs but, in contrast to anti-TNFα treatment, anti-IL12B shRNAs do not ameliorate the psoriatic phenotype as evaluated by semi-quantitative clinical scoring and by immunohistological examination.</p> <p>Conclusions</p> <p>Our studies consolidate the properties of lentiviral vectors as a tool for potent gene delivery and for evaluation of mRNA targets for anti-inflammatory therapy. However, in contrast to local anti-TNFα treatment, the therapeutic potential of targeting IL-12B at the RNA level in psoriasis is questioned.</p

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework

Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons

The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience