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Cysteine cross-linking in native membranes establishes the transmembrane architecture of Ire1
(2021)
The ER is a key organelle of membrane biogenesis and crucial for the folding of both membrane and secretory proteins. Sensors of the unfolded protein response (UPR) monitor the unfolded protein load in the ER and convey effector functions for maintaining ER homeostasis. Aberrant compositions of the ER membrane, referred to as lipid bilayer stress, are equally potent activators of the UPR. How the distinct signals from lipid bilayer stress and unfolded proteins are processed by the conserved UPR transducer Ire1 remains unknown. Here, we have generated a functional, cysteine-less variant of Ire1 and performed systematic cysteine cross-linking experiments in native membranes to establish its transmembrane architecture in signaling-active clusters. We show that the transmembrane helices of two neighboring Ire1 molecules adopt an X-shaped configuration independent of the primary cause for ER stress. This suggests that different forms of stress converge in a common, signaling-active transmembrane architecture of Ire1.
Using the eigenchannel reaction theory we performed coupled-channel calculations for Si28 and computed the differential cross section for Al27(p, γ0)Si28 over the energy range 6 MeV<Ep <16 MeV. The obtained angular distributions are nearly constant over the whole energy range and agree with the experiment in that they are almost isotropic. Thus, it seems that in this framework we can give a natural explanation for the peculiar behavior of the Al27(p, γ0)Si28 cross section.
The total particle-particle SJ matrix of O16 for spin J=1- and excitation energies between 15 and 27 MeV has been calculated in the eigenchannel reaction theory for several parameters of the Saxon-Woods potential and the two-body force. The many-body problem has been treated in the 1-particle-1-hole approximation. The photon channels have been included by perturbation theory. Surprisingly, the most important structure of the experimental cross sections is reproduced quite well in this simple approximation.
The toolbox for imaging molecules is well-equipped today. Some techniques visualize the geometrical structure, others the electron density or electron orbitals. Molecules are many-body systems for which the correlation between the constituents is decisive and the spatial and the momentum distribution of one electron depends on those of the other electrons and the nuclei. Such correlations have escaped direct observation by imaging techniques so far. Here, we implement an imaging scheme which visualizes correlations between electrons by coincident detection of the reaction fragments after high energy photofragmentation. With this technique, we examine the H2 two-electron wave function in which electron–electron correlation beyond the mean-field level is prominent. We visualize the dependence of the wave function on the internuclear distance. High energy photoelectrons are shown to be a powerful tool for molecular imaging. Our study paves the way for future time resolved correlation imaging at FELs and laser based X-ray sources.
We analyze the phase structure of the nonlinear mean-field meson theory of baryonic matter (nucleons plus delta resonances). Depending on the choice of the coupling constants, we find three physically distinct phase transitions in this theory: a nucleonic liquid-gas transition in the low temperature, Tc<20 MeV, low density, ρ≃0.5ρ0, regime, a high-temperature (T≃150 MeV) finite density transition from a gas of massive hadrons to a nearly massless baryon, antibaryon plasma, and, third, a strong phase transition from the nucleonic fluid to a resonance-dominated ‘‘delta-matter’’ isomer at ρ>2ρ0 and Tc<50 MeV. All three phase transitions are of first order. It is shown that the occurrence of these different phase transitions depends critically on the coupling constants. Since the production of pions also depends strongly on the coupling constants, it is seen that the equation of state cannot be derived unambiguously from pion data.
We investigated the excitation of surface plasmon polaritons on gold films with the metallized probe tip of a scattering-type scanning near-field optical microscope (s-SNOM). The emission of the polaritons from the tip, illuminated by near-infrared laser radiation, was found to be anisotropic and not circularly symmetric as expected on the basis of literature data. We furthermore identified an additional excitation channel via light that was reflected off the tip and excited the plasmon polaritons at the edge of the metal film. Our results, while obtained for a non-rotationally-symmetric type of probe tip and thus specific for this situation, indicate that when an s-SNOM is employed for the investigation of plasmonic structures, the unintentional excitation of surface waves and anisotropic surface wave propagation must be considered in order to correctly interpret the signatures of plasmon polariton generation and propagation.
Low-level-laser therapy (LLLT) is an effective complementary treatment, especially for anti-inflammation and wound healing in which dermis or mucus mast cells (MCs) are involved. In periphery, MCs crosstalk with neurons via purinergic signals and participate in various physiological and pathophysiological processes. Whether extracellular ATP, an important purine in purinergic signaling, of MCs and neurons could be modulated by irradiation remains unknown. In this study, effects of red-laser irradiation on extracellular ATP content of MCs and dorsal root ganglia (DRG) neurons were investigated and underlying mechanisms were explored in vitro. Our results show that irradiation led to elevation of extracellular ATP level in the human mast cell line HMC-1 in a dose-dependent manner, which was accompanied by elevation of intracellular ATP content, an indicator for ATP synthesis, together with [Ca2+]i elevation, a trigger signal for exocytotic ATP release. In contrast to MCs, irradiation attenuated the extracellular ATP content of neurons, which could be abolished by ARL 67156, a nonspecific ecto-ATPases inhibitor. Our results suggest that irradiation potentiates extracellular ATP of MCs by promoting ATP synthesis and release and attenuates extracellular ATP of neurons by upregulating ecto-ATPase activity. The opposite responses of these two cell types indicate complex mechanisms underlying LLLT.
In Chinese medicine acupuncture points are treated by physical stimuli to counteract various diseases. These stimuli include mechanical stress as applied during the needle manipulation or tuina, high temperatures as applied during moxibustion, and red laser light applied during laser acupuncture. This study aimed to investigate cellular responses to stimuli that might occur in the tissue of acupuncture points. Since they have a characteristically high density of mast cells that degranulate in response to acupuncture, we asked whether these processes lead to ATP release. We tested in in vitro experiments on mast cells of the human mast-cell line HMC-1 the effects of the physical stimuli; mechanical stress was applied by superfusion of the cells with hypotonic solution, heat was applied by incubation of the cells at 52°C, and red laser light of 657 nm was used for irradiation. We demonstrate that all the stimuli induce ATP release from model human mast HMC-1 cells, and this release is associated with an intracellular free Ca2+ rise. We hypothesize that ATP released from mast cells supplements the already known release of ATP from keratinocytes and, by acting on P2X receptors, it may serve as initial mediator of acupuncture-induced analgesia.
In this proceeding, the deep Convolutional Neural Networks(CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic process, we treat the final spectra as image-type inputs which preserve sufficient spatiotemporal correlations. As a practical example, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a 1+1-dimensional Langevin equation for σ field. The well-trained CNNs accurately classify the first-order phase transition and crossover from σ field configurations with fluctuations, in which the noise does not impair the performance of the recognition. In reconstructing the dynamics, we demonstrate it is robust to extract the damping coefficients η from the intricate field configurations.
The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.
We present a new type of flow analysis, based on a particle-pair correlation function, in which there is no need for an event-by-event determination of the reaction plane. Consequently, the need to correct for dispersion in an estimated reaction plane does not arise. Our method also offers the option to avoid any influence from particle misidentification. Using this method, streamer chamber data for collisions of Ar+KCl and Ar+BaI2 at 1.2 GeV/nucleon are compared with predictions of a nuclear transport model.
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff Esym(ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.
Complex I couples the free energy released from quinone (Q) reduction to pump protons across the biological membrane in the respiratory chains of mitochondria and many bacteria. The Q reduction site is separated by a large distance from the proton-pumping membrane domain. To address the molecular mechanism of this long-range proton-electron coupling, we perform here full atomistic molecular dynamics simulations, free energy calculations, and continuum electrostatics calculations on complex I from Thermus thermophilus. We show that the dynamics of Q is redox-state-dependent, and that quinol, QH2, moves out of its reduction site and into a site in the Q tunnel that is occupied by a Q analog in a crystal structure of Yarrowia lipolytica. We also identify a second Q-binding site near the opening of the Q tunnel in the membrane domain, where the Q headgroup forms strong interactions with a cluster of aromatic and charged residues, while the Q tail resides in the lipid membrane. We estimate the effective diffusion coefficient of Q in the tunnel, and in turn the characteristic time for Q to reach the active site and for QH2 to escape to the membrane. Our simulations show that Q moves along the Q tunnel in a redox-state-dependent manner, with distinct binding sites formed by conserved residue clusters. The motion of Q to these binding sites is proposed to be coupled to the proton-pumping machinery in complex I.
We analyzed a eukaryotically encoded rubredoxin from the cryptomonad Guillardia theta and identified additional domains at the N- and C-termini in comparison to known prokaryotic paralogous molecules. The cryptophytic N-terminal extension was shown to be a transit peptide for intracellular targeting of the protein to the plastid, whereas a C-terminal domain represents a membrane anchor. Rubredoxin was identified in all tested phototrophic eukaryotes. Presumably facilitated by its C-terminal extension, nucleomorph-encoded rubredoxin (nmRub) is associated with the thylakoid membrane. Association with photosystem II (PSII) was demonstrated by co-localization of nmRub and PSII membrane particles and PSII core complexes and confirmed by comparative electron paramagnetic resonance measurements. The midpoint potential of nmRub was determined as +125 mV, which is the highest redox potential of all known rubredoxins. Therefore, nmRub provides a striking example of the ability of the protein environment to tune the redox potentials of metal sites, allowing for evolutionary adaption in specific electron transport systems, as for example that coupled to the PSII pathway.
Formation of Hubbard-like bands as a fingerprint of strong electron-electron interactions in FeSe
(2017)
We use angle-resolved photo-emission spectroscopy (ARPES) to explore the electronic structure of single crystals of FeSe over a wide range of binding energies and study the effects of strong electron-electron correlations. We provide evidence for the existence of "Hubbard-like bands" at high binding energies consisting of incoherent many-body excitations originating from Fe 3d states in addition to the renormalized quasiparticle bands near the Fermi level. Many high energy features of the observed ARPES data can be accounted for when incorporating effects of strong local Coulomb interactions in calculations of the spectral function via dynamical mean-field theory, including the formation of a Hubbard-like band. This shows that over the energy scale of several eV, local correlations arising from the on-site Coulomb repulsion and Hund's coupling are essential for a proper understanding of the electronic structure of FeSe and other related iron based superconductors.
The amount of proton stopping in central Pb+Pb collisions from 20–160 A GeV as well as hyperon and antihyperon rapidity distributions are calculated within the UrQMD model in comparison to experimental data at 40, 80, and 160 A GeV taken recently from the NA49 collaboration. Furthermore, the amount of baryon stopping at 160A GeV for Pb+Pb collisions is studied as a function of centrality in comparison to the NA49 data. We find that the strange baryon yield is reasonably described for central collisions, however, the rapidity distributions are somewhat more narrow than the data. Moreover, the experimental antihyperon rapidity distributions at 40, 80, and 160 A GeV are underestimated by up to factors of 3—depending on the annihilation cross section employed—which might be addressed to missing multimeson fusion channels in the UrQMD model. Pacs-Nr.: 25.75.2q, 24.10.Jv, 24.10.Lx
We derive the collision term in the Boltzmann equation using the equation of motion for the Wigner function of massive spin-1/2 particles. To next-to-lowest order in h, it contains a nonlocal contribution, which is responsible for the conversion of orbital into spin angular momentum. In a proper choice of pseudogauge, the antisymmetric part of the energy-momentum tensor arises solely from this nonlocal contribution. We show that the collision term vanishes in global equilibrium and that the spin potential is, then, equal to the thermal vorticity. In the nonrelativistic limit, the equations of motion for the energy-momentum and spin tensors reduce to the well-known form for hydrodynamics for micropolar fluids.
P-type ATPases are membrane proteins acting as ion pumps that drive an active transport of cations across the membrane against a concentration gradient. The required energy for the ion transport is provided by binding and hydrolysis of ATP. A reaction mechanism of ion transport and energy transduction is assumed to be common for all P-type ATPases and generally described by the Post-Albers cycle. Transient currents and charge translocation of P-type ATPases were extensively investigated by electrical measurements that apply voltage jumps to initiate the reaction cycle. In this study, we simulate an applied voltage across the membrane by an electric field and perform electrostatic calculations in order to verify the experimentally-driven hypothesis that the energy transduction mechanism is regulated by specific structural elements. Side chain conformational and ionization changes induced by the electric field are evaluated for each transmembrane helix and the selectivity in response is qualitatively analyzed for the Ca2+-ATPase as well as for structural models of the Na+/K+-ATPase. Helix M5 responds with more conformer changes as compared to the other transmembrane helices what is even more emphasized when the stalk region is included. Thus our simulations support experimental results and indicate a crucial role for the highly conserved transmembrane helix M5 in the energy transduction mechanism of P-type ATPases.