Structural Determination of Functional Units of the Nucleotide Binding Domain (NBD94) of the Reticulocyte Binding Protein Py235 of Plasmodium yoelii

Invasion of the red blood cells (RBC) by the merozoite of malaria parasites involves a large number of receptor ligand interactions. The reticulocyte binding protein homologue family (RH) plays an important role in erythrocyte recognition as well as virulence. Recently, it has been shown that members of RH in addition to receptor binding may also have a role as ATP/ADP sensor. A 94 kDa region named Nucleotide-Binding Domain 94 (NBD94) of Plasmodium yoelii YM, representative of the putative nucleotide binding region of RH, has been demonstrated to bind ATP and ADP selectively. Binding of ATP or ADP induced nucleotide-dependent structural changes in the C-terminal hinge-region of NBD94, and directly impacted on the RBC binding ability of RH.In order to find the smallest structural unit, able to bind nucleotides, and its coupling module, the hinge region, three truncated domains of NBD94 have been generated, termed NBD94(444-547), NBD94(566-663) and NBD94(674-793), respectively. Using fluorescence correlation spectroscopy NBD94(444-547) has been identified to form the smallest nucleotide binding segment, sensitive for ATP and ADP, which became inhibited by 4-Chloro-7-nitrobenzofurazan. The shape of NBD94(444-547) in solution was calculated from small-angle X-ray scattering data, revealing an elongated molecule, comprised of two globular domains, connected by a spiral segment of about 73.1 A in length. The high quality of the constructs, forming the hinge-region, NBD94(566-663) and NBD94(674-793) enabled to determine the first crystallographic and solution structure, respectively. The crystal structure of NBD94(566-663) consists of two helices with 97.8 A and 48.6 A in length, linked by a loop. By comparison, the low resolution structure of NBD94(674-793) in solution represents a chair-like shape with three architectural segments.These structures give the first insight into how nucleotide binding impacts on the overall structure of RH and demonstrates the potential use of this region as a novel drug target

Correction: Contribution of the Cpx envelope stress system to metabolism and virulence regulation in Salmonella enterica serovar Typhimurium

[This corrects the article DOI: 10.1371/journal.pone.0211584.]

Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology:Preliminary results

A dynamic model is constructed for interactive silicon, nitrogen, sulfur processing in and below Arctic sea ice, by ecosystems residing in the lower few centimeters of the distributed pack. A biogeochemically active bottom layer supporting sources/sinks for the pennate diatoms is appended to thickness categories of a global sea ice code. Nutrients transfer from the ocean mixed layer to drive algal growth, while sulfur metabolites are reinjected from the ice interface. Freeze, flux, flush and melt processes are linked to multielement geocycling for the entire high-latitude regime. Major element kinetics are optimized initially to reproduce chlorophyll observations, which extend across the seasons. Principal influences on biomass are solute exchange velocity at the solid interface, optical averaging in active ice and cell retention against ablation. The sulfur mechanism encompasses open water features such as accumulation of particulate dimethyl sulfoniopropionate, grazing and other disruptive releases, plus bacterial/enzymatic conversion to volatile dimethyl sulfide. For baseline settings, the mixed layer trace gas distribution matches sparging measurements where they are available. However, concentrations rise to well over 10 nM in remote, unsampled locations. Peak contributions are supported by ice grazing, mortality and fractional melting. The model bottom layer adds substantially to a ring maximum of reduced sulfur chemistry that may be dominant across the marginal Arctic environment. Sensitivity tests on this scenario include variation of cell sulfur composition and remineralization, routings/chemical time scales, and the physical dimension of water layers. An alternate possibility that peripheral additions are small cannot be excluded from the outcomes. It is concluded that seagoing dimethyl sulfide data are far too sparse at the present time to distinguish sulfur-ice production levels. Citation: Elliott, S., C. Deal, G. Humphries, E. Hunke, N. Jeffery, M. Jin, M. Levasseur, and J. Stefels (2012), Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology: Preliminary results, J. Geophys. Res., 117, G01016, doi:10.1029/2011JG001649

Sea level variability in the Arctic Ocean from AOMIP models

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): C04S08, doi:10.1029/2006JC003916.Monthly sea levels from five Arctic Ocean Model Intercomparison Project (AOMIP) models are analyzed and validated against observations in the Arctic Ocean. The AOMIP models are able to simulate variability of sea level reasonably well, but several improvements are needed to reduce model errors. It is suggested that the models will improve if their domains have a minimum depth less than 10 m. It is also recommended to take into account forcing associated with atmospheric loading, fast ice, and volume water fluxes representing Bering Strait inflow and river runoff. Several aspects of sea level variability in the Arctic Ocean are investigated based on updated observed sea level time series. The observed rate of sea level rise corrected for the glacial isostatic adjustment at 9 stations in the Kara, Laptev, and East Siberian seas for 1954–2006 is estimated as 0.250 cm/yr. There is a well pronounced decadal variability in the observed sea level time series. The 5-year running mean sea level signal correlates well with the annual Arctic Oscillation (AO) index and the sea level atmospheric pressure (SLP) at coastal stations and the North Pole. For 1954–2000 all model results reflect this correlation very well, indicating that the long-term model forcing and model reaction to the forcing are correct. Consistent with the influences of AO-driven processes, the sea level in the Arctic Ocean dropped significantly after 1990 and increased after the circulation regime changed from cyclonic to anticyclonic in 1997. In contrast, from 2000 to 2006 the sea level rose despite the stabilization of the AO index at its lowest values after 2000.This research is supported by the National Science Foundation Office of Polar Programs (under cooperative agreements OPP- 0002239 and OPP- 0327664) with the International Arctic Research Center, University of Alaska Fairbanks, and by the Climate Change Prediction Program of the Department of Energy’s Office of Biological and Environmental Research. The development of the UW model is also supported by NASA grants NNG04GB03G and NNG04GH52G and NSF grants OPP-0240916 and OPP-0229429

Exploring viscosity space in an eddy‐permitting global ocean model: Is viscosity a useful control for numerical mixing?

A generic shortcoming of constant-depth (or “z-coordinate”) ocean models such as MOM5 and Nucleus for European Models of the Ocean (NEMO) is a tendency for the advection scheme to produce unphysical numerical diapycnal mixing, which may exceed the explicitly parameterized mixing based on observed physical processes. Megann (2018, https://doi.org/10.1016/j.ocemod.2017.11.001) estimated the effective diapycnal diffusivity in the Global Ocean Version 5.0 (GO5.0) 0.25° global implementation of the NEMO model and showed that this was up to 10 times the explicit diffusivity used in the model's mixing scheme and argued that this was at least partly caused by large transient vertical velocities on length scales comparable to the horizontal grid scale. The current UK global NEMO configuration GO6, as used in the Global Coupled Model version 3.1 (GC3.1) and UK Earth System Model (UKESM1), is integrated in forced mode at 0.25° resolution with a range of viscosity parameterizations. In the present study, the effective diffusivity is evaluated for each integration and compared with the explicit value from the model mixing scheme, as well as with that in the control (using the default viscosity). It is shown that there is a strong correspondence between lower viscosity and enhanced numerical mixing and that larger viscosities lead to a marked reduction in the unrealistic internal temperature drift seen in the control configuration, without incurring excessive damping of the large-scale circulation, mixed layer depths, or sea ice cover. The results presented here will inform the choices made in global ocean configurations used in climate and Earth System models following the sixth Coupled Model Intercomparison Project (CMIP6)

Study of the impact of ice formation in leads upon the sea ice pack mass balance using a new frazil and grease ice parameterization

Leads are cracks in sea ice that often form because of deformation. During winter months, leads expose the ocean to the cold atmosphere, resulting in supercooling and the formation of frazil ice crystals within the mixed layer. Here the authors investigate the role of frazil ice formation in leads on the mass balance of the sea ice pack through the incorporation of a new module into the Los Alamos sea ice model (CICE). The frazil ice module considers an initial cooling of leads followed by a steady-state formation of uniformly distributed single size frazil ice crystals that precipitate to the ocean surface as grease ice. The grease ice is pushed against one of the lead edges by wind and water drag that the authors represent through a variable collection thickness for new sea ice. Simulations of the sea ice cover in the Arctic and Antarctic are performed and compared to a model that treats leads the same as the open ocean. The processes of ice formation in the new module slow down the refreezing of leads, resulting in a longer period of frazil ice production. The fraction of frazil-derived sea ice increases from 10% to 50%, corresponding better to observations. The new module has higher ice formation rates in areas of high ice concentration and thus has a greater impact within multiyear ice than it does in the marginal seas. The thickness of sea ice in the central Arctic increases by over 0.5 m, whereas within the Antarctic it remains unchanged

Atmospheric forcing validation for modeling the central Arctic

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 34 (2007): L20706, doi:10.1029/2007GL031378.We compare daily data from the National Center for Atmospheric Research and National Centers for Environmental Prediction “Reanalysis 1” project with observational data obtained from the North Pole drifting stations in order to validate the atmospheric forcing data used in coupled ice-ocean models. This analysis is conducted to assess the role of errors associated with model forcing before performing model verifications against observed ocean variables. Our analysis shows an excellent agreement between observed and reanalysis sea level pressures and a relatively good correlation between observed and reanalysis surface winds. The observed temperature is in good agreement with reanalysis data only in winter. Specific air humidity and cloudiness are not reproduced well by reanalysis and are not recommended for model forcing. An example sensitivity study demonstrates that the equilibrium ice thickness obtained using NP forcing is two times thicker than using reanalysis forcing.This research is supported by the National Science Foundation Office of Polar Programs (under Cooperative Agreements Nos. OPP-0002239 and OPP-0327664) with the International Arctic Research Center, University of Alaska Fairbanks, NSF grant OPP- 0424864 and by Russian Foundation for Basic Research, No. 07-05-13576

Should sea-ice modeling tools designed for climate research be used for short-term forecasting?

In theory, the same sea-ice models could be used for both research and operations, but in practice, differences in scientific and software requirements and computational and human resources complicate the matter. Although sea-ice modeling tools developed for climate studies and other research applications produce output of interest to operational forecast users, such as ice motion, convergence, and internal ice pressure, the relevant spatial and temporal scales may not be sufficiently resolved. For instance, sea-ice research codes are typically run with horizontal resolution of more than 3 km, while mariners need information on scales less than 300 m. Certain sea-ice processes and coupled feedbacks that are critical to simulating the Earth system may not be relevant on these scales; and therefore, the most important model upgrades for improving sea-ice predictions might be made in the atmosphere and ocean components of coupled models or in their coupling mechanisms, rather than in the sea-ice model itself. This paper discusses some of the challenges in applying sea-ice modeling tools developed for research purposes for operational forecasting on short time scales, and highlights promising new directions in sea-ice modeling