The canonical O-mannosylation pathway in humans is essential for the functional glycosylation of α-dystroglycan. Disruption of this post-translational modification pathway leads to congenital muscular dystrophies. The first committed step in the construction of a functional matriglycan structure involves the post-translational modification of α-dystroglycan. This is essential for binding extracellular matrix proteins and arenaviruses, and is catalyzed by β-1,4-N-acetylglucosaminyltransferase 2 (POMGNT2). While another glycosyl transferase, β-1,4-N-acetylglucosaminyltransferase 1 (POMGNT1), has been shown to be promiscuous in extending O-mannosylated sites, POMGNT2 has been shown to display significant primary amino-acid selectivity near the site of O-mannosylation. Moreover, several single point mutations in POMGNT2 have been identified in patients with assorted dystroglycanopathies such as Walker-Warburg syndrome and limb girdle muscular dystrophy. To gain insight into POMGNT2 function in humans, the enzyme was expressed as a soluble, secreted fusion protein by transient infection of HEK293 suspension cultures. Here, crystal structures of POMGNT2 (amino-acid residues 25-580) with and without UDP bound are reported. Consistent with a novel fold and a unique domain organization, no molecular-replacement model was available and phases were obtained through crystallization of a selenomethionine variant of the enzyme in the same space group. Tetragonal (space group P422; unit-cell parameters a = b = 129.8, c = 81.6 Å, α = γ = β = 90°) crystals with UDP bound diffracted to 1.98 Å resolution and contained a single monomer in the asymmetric unit. Orthorhombic (space group P222; unit-cell parameters a = 142.3, b = 153.9, c = 187.4 Å, α = γ = β = 90°) crystals were also obtained; they diffracted to 2.57 Å resolution and contained four monomers with differential glycosylation patterns and conformations. These structures provide the first rational basis for an explanation of the loss-of-function mutations and offer significant insights into the mechanics of this important human enzyme.
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http://dx.doi.org/10.1107/S2059798321001261 | DOI Listing |
Neuroradiology
December 2024
Department of Radiology, Binzhou Medical University, Zibo Central Hospital, Zibo, China.
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View Article and Find Full Text PDFAngew Chem Int Ed Engl
December 2024
Indian Institute of Science Education and Research Pune, Chemistry, Dr. Homi Bhabha Road, 411008, Pune, INDIA.
Two-dimensional (2D) chiral hybrid perovskites A2PbI4 (A: chiral organic ion) enable chirality controlled optoelectronic and spin-based properties. A+ organic sublattice induces chirality into the semiconducting [PbI4]2- inorganic sublattice through non-covalent interactions at organic-inorganic interface. Often, the A+ cations in the lattice have different orientations, leading to asymmetry in the non-covalent interactions.
View Article and Find Full Text PDFDalton Trans
December 2024
Ordered Matter Science Research Center, Nanchang University, 330031, P. R. China.
Organic-inorganic hybrid metal halides (OIMHs) with ferroelastic phase transition properties have recently attracted great attention due to their widespread application prospects in the fields of energy storage, sensors, switches, . However, most of the hybrid ferroelastics exhibit phase transition points () far beyond room temperature, which may limit their applications in mechanical switches and energy storage for daily working requirements. Herein, we synthesized a new zinc halide OIMH ferroelastic (,)-[BPHD]ZnBr (BPHD = 1,6-bis(piperidine-1-yl) hexa-2,4-diene diamide), which experiences a 2/1̄ type paraelastic-ferroelastic phase transition at a near-room-temperature of 285 K.
View Article and Find Full Text PDFJMIR Ment Health
December 2024
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany.
Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.
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