Development and Validation of a Fast and Homogeneous Cell-Based Fluorescence Screening Assay for Divalent Metal Transporter 1 (DMT1/SLC11A2) Using the FLIPR Tetra.

Divalent metal ion transporter 1 (DMT1) is a proton-coupled Fe(2+) transporter that is essential for iron uptake in enterocytes and for transferrin-associated endosomal iron transport in many other cell types. DMT1 dysfunction is associated with several diseases such as iron overload disorders and neurodegenerative diseases. The main objective of the present work is to develop and validate a fluorescence-based screening assay for DMT1 modulators. We found that Fe(2+) or Cd(2+) influx could be reliably monitored in calcium 5-loaded DMT1-expressing HEK293 cells using the FLIPR Tetra fluorescence microplate reader. DMT1-mediated metal transport shows saturation kinetics depending on the extracellular substrate concentration, with a K0.5 value of 1.4 µM and 3.5 µM for Fe(2+) and Cd(2+), respectively. In addition, Cd(2+) was used as a substrate for DMT1, and we find a Ki value of 2.1 µM for a compound (2-(3-carbamimidoylsulfanylmethyl-benzyl)-isothiourea) belonging to the benzylisothioureas family, which has been identified as a DMT1 inhibitor. The optimized screening method using this compound as a reference demonstrated a Z' factor of 0.51. In summary, we developed and validated a sensitive and reproducible cell-based fluorescence assay suitable for the identification of compounds that specifically modulate DMT1 transport activity.

SUSYFLAVOR v2: A Computational tool for FCNC and CP-violating processes in the MSSM

We present SUSY_FLAVOR version 2 — a Fortran 77 program that calculates low-energy flavor observables in the general R-parity conserving MSSM. For a set of MSSM parameters as input, the code gives predictions for: 1. Electric dipole moments of the leptons and the neutron. 2. Anomalous magnetic moments (i.e. g − 2) of the leptons. 3. Radiative lepton decays (μ → eγ and τ → μγ , eγ ). 4. Rare Kaon decays (K0 L → π0 ¯νν and K+ → π+ ¯νν). 5. Leptonic B decays (Bs,d → l+l−, B → τ ν and B → Dτ ν). 6. Radiative B decays (B → ¯ Xsγ ). 7. ΔF = 2 processes ( ¯ K0–K0, ¯D–D, ¯Bd–Bd and ¯Bs–Bs mixing). Comparing to SUSY_FLAVOR v1, where the matching conditions were calculated strictly at one-loop level, SUSY_FLAVOR v2 performs the resummation of all chirally enhanced corrections, i.e. takes into account the enhanced effects from tan β and/or large trilinear soft mixing terms to all orders in perturbation theory. Also, in SUSY_FLAVOR v2 new routines calculation of B → (D)τ ν, g − 2, radiative lepton decays and Br(l → l′γ ) were added. All calculations are done using exact diagonalization of the sfermion mass matrices. The program can be obtained from http://www.fuw.edu.pl/susy_flavor.

Anisotropy of magnetic susceptibility in natural olivine single crystals

Mantle flow dynamics can cause preferential alignment of olivine crystals that results in anisotropy of physical properties. To interpret anisotropy in mantle rocks, it is necessary to understand the anisotropy of olivine single crystals. We determined anisotropy of magnetic susceptibility (AMS) for natural olivine crystals. High-field AMS allows for the isolation of the anisotropy due to olivine alone. The orientations of the principal susceptibility axes are related to the olivine’s crystallographic structure as soon as it contains >3 wt % FeO. The maximum susceptibility is parallel to the c axis both at room temperature (RT) and at 77 K. The orientation of the minimum axis at RT depends on iron content; it is generally parallel to the a axis in crystals with 3–5 wt % FeO, and along b in samples with 6–10 wt % FeO. The AMS ellipsoid is prolate and the standard deviatoric susceptibility, k0, is on the order of 8*10210 m3/kg for the samples with <1wt % FeO, and ranges from 3.1*1029 m3/kg to 5.7*1029 m3/kg for samples with 3–10 wt % FeO. At 77 K, the minimum susceptibility is along b, independent of iron content. The shape of the AMS ellipsoid is prolate for samples with <5 wt % FeO, but can be prolate or oblate for higher iron content. The degree of anisotropy increases at 77 K with p0 7757.160.5. The results from this study will allow AMS fabrics to be used as a proxy for olivine texture in ultramafic rocks with high olivine content.

Differences in grain growth of calcite: a field-based modeling approach

Normal grain growth of calcite was investigated by combining grain size analysis of calcite across the contact aureole of the Adamello pluton, and grain growth modeling based on a thermal model of the surroundings of the pluton. In an unbiased model system, i.e., location dependent variations in temperature-time path, 2/3 and 1/3 of grain growth occurs during pro- and retrograde metamorphism at all locations, respectively. In contrast to this idealized situation, in the field example three groups can be distinguished, which are characterized by variations in their grain size versus temperature relationships: Group I occurs at low temperatures and the grain size remains constant because nano-scale second phase particles of organic origin inhibit grain growth in the calcite aggregates under these conditions. In the presence of an aqueous fluid, these second phases decay at a temperature of about 350 °C enabling the onset of grain growth in calcite. In the following growth period, fluid-enhanced group II and slower group III growth occurs. For group II a continuous and intense grain size increase with T is typical while the grain growth decreases with T for group III. None of the observed trends correlate with experimentally based grain growth kinetics, probably due to differences between nature and experiment which have not yet been investigated (e.g., porosity, second phases). Therefore, grain growth modeling was used to iteratively improve the correlation between measured and modeled grain sizes by optimizing activation energy (Q), pre-exponential factor (k0) and grain size exponent (n). For n=2, Q of 350 kJ/mol, k0 of 1.7×1021 μmns−1 and Q of 35 kJ/mol, k0 of 2.5×10-5 μmns−1 were obtained for group II and III, respectively. With respect to future work, field-data based grain growth modeling might be a promising tool for investigating the influences of secondary effects like porosity and second phases on grain growth in nature, and to unravel differences between nature and experiment.