Characterization of the First Secreted Sorting Nexin Identified in the Leishmania Protists.

Proteins of the sorting nexin (SNX) family present a modular structural architecture with a phox homology (PX) phosphoinositide (PI)-binding domain and additional PX structural domains, conferring to them a wide variety of vital eukaryotic cell's functions, from signal transduction to membrane deformation and cargo binding. Although SNXs are well studied in human and yeasts, they are poorly investigated in protists. Herein, is presented the characterization of the first SNX identified in Leishmania protozoan parasites encoded by the LdBPK_352470 gene. In silico secondary and tertiary structure prediction revealed a PX domain on the N-terminal half and a Bin/amphiphysin/Rvs (BAR) domain on the C-terminal half of this protein, with these features classifying it in the SNX-BAR subfamily of SNXs. We named the LdBPK_352470.1 gene product LdSNXi, as it is the first SNX identified in Leishmania (L.) donovani. Its expression was confirmed in L. donovani promastigotes under different cell cycle phases, and it was shown to be secreted in the extracellular medium. Using an in vitro lipid binding assay, it was demonstrated that recombinant (r) LdSNXi (rGST-LdSNXi) tagged with glutathione-S-transferase (GST) binds to the PtdIns3P and PtdIns4P PIs. Using a specific a-LdSNXi antibody and immunofluorescence confocal microscopy, the intracellular localization of endogenous LdSNXi was analyzed in L. donovani promastigotes and axenic amastigotes. Additionally, rLdSNXi tagged with enhanced green fluorescent protein (rLdSNXi-EGFP) was heterologously expressed in transfected HeLa cells and its localization was examined. All observed localizations suggest functions compatible with the postulated SNX identity of LdSNXi. Sequence, structure, and evolutionary analysis revealed high homology between LdSNXi and the human SNX2, while the investigation of protein-protein interactions based on STRING (v.11.5) predicted putative molecular partners of LdSNXi in Leishmania.

Magnetic skyrmions in FePt nanoparticles having Reuleaux 3D geometry: a micromagnetic simulation study.

The magnetization reversal in magnetic FePt nanoelements having Reuleaux 3D geometry is studied using micromagnetic simulations employing Finite Element discretizations. Magnetic skyrmions are revealed in different systems generated by the variation of the magnitude of the magnetocrystalline anisotropy which was kept normal to the nanoelement's base and parallel to the applied external field. The topological quantity of skyrmion number is computed in order to characterize micromagnetic configurations exhibiting skyrmionic formations. Micromagnetic configurations with a wide range of skyrmion numbers between -3 and 3 are indicative for the existence of one or multiple skyrmions that have been detected and stabilized in a range of external fields. Internal magnetic structures are shown consisting of Bloch type skyrmionic entities in the bulk altered to Néel skyrmions on the nanoelement's bottom and top base surfaces. The actual sizes of the formed skyrmions and the internal magnetization structures were computed. In particular, the sizes of the generated and persistent skyrmions were calculated as functions of the magnetocrystalline anisotropy value and of the applied external magnetic field. It is shown that the size of skyrmions is linearly dependent on the external field value. The slope of the linear curve can be controlled by the magnetocrystalline anisotropy value. The magnetic skyrmions can be created for FePt magnetic systems lacking of chiral interactions by designing the geometry-shape of the nanoparticle and by controlling the value of magnetocrystalline anisotropy.

Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction.

Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.