Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia.

Cerium-stabilized zirconia (Ce1-xZrxOy, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (Ov) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest Ov diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable Ov pairs along the ⟨110⟩ direction, which reduces the energy barrier of Ov diffusion in the bulk and promotes O2 activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications.

Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential.

Liquid-liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid's atomic arrangements. When examining CdSxTe1-x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x > 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x < 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system's behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes.

Identification of existing pharmaceuticals and herbal medicines as inhibitors of SARS-CoV-2 infection.

The outbreak of COVID-19 caused by SARS-CoV-2 has resulted in more than 50 million confirmed cases and over 1 million deaths worldwide as of November 2020. Currently, there are no effective antivirals approved by the Food and Drug Administration to contain this pandemic except the antiviral agent remdesivir. In addition, the trimeric spike protein on the viral surface is highly glycosylated and almost 200,000 variants with mutations at more than 1,000 positions in its 1,273 amino acid sequence were reported, posing a major challenge in the development of antibodies and vaccines. It is therefore urgently needed to have alternative and timely treatments for the disease. In this study, we used a cell-based infection assay to screen more than 3,000 agents used in humans and animals, including 2,855 small molecules and 190 traditional herbal medicines, and identified 15 active small molecules in concentrations ranging from 0.1 nM to 50 µM. Two enzymatic assays, along with molecular modeling, were then developed to confirm those targeting the virus 3CL protease and the RNA-dependent RNA polymerase. Several water extracts of herbal medicines were active in the cell-based assay and could be further developed as plant-derived anti-SARS-CoV-2 agents. Some of the active compounds identified in the screen were further tested in vivo, and it was found that mefloquine, nelfinavir, and extracts of Ganoderma lucidum (RF3), Perilla frutescens, and Mentha haplocalyx were effective in a challenge study using hamsters as disease model.

Amorphous Chloride Solid Electrolytes with High Li-Ion Conductivity for Stable Cycling of All-Solid-State High-Nickel Cathodes.

Solid electrolytes (SEs) are central components that enable high-performance, all-solid-state lithium batteries (ASSLBs). Amorphous SEs hold great potential for ASSLBs because their grain-boundary-free characteristics facilitate intact solid-solid contact and uniform Li-ion conduction for high-performance cathodes. However, amorphous oxide SEs with limited ionic conductivities and glassy sulfide SEs with narrow electrochemical windows cannot sustain high-nickel cathodes. Herein, we report a class of amorphous Li-Ta-Cl-based chloride SEs possessing high Li-ion conductivity (up to 7.16 mS cm-1) and low Young's modulus (approximately 3 GPa) to enable excellent Li-ion conduction and intact physical contact among rigid components in ASSLBs. We reveal that the amorphous Li-Ta-Cl matrix is composed of LiCl43-, LiCl54-, LiCl65- polyhedra, and TaCl6- octahedra via machine-learning simulation, solid-state 7Li nuclear magnetic resonance, and X-ray absorption analysis. Attractively, our amorphous chloride SEs exhibit excellent compatibility with high-nickel cathodes. We demonstrate that ASSLBs comprising amorphous chloride SEs and high-nickel single-crystal cathodes (LiNi0.88Co0.07Mn0.05O2) exhibit ∼99% capacity retention after 800 cycles at ∼3 C under 1 mA h cm-2 and ∼80% capacity retention after 75 cycles at 0.2 C under a high areal capacity of 5 mA h cm-2. Most importantly, a stable operation of up to 9800 cycles with a capacity retention of ∼77% at a high rate of 3.4 C can be achieved in a freezing environment of -10 °C. Our amorphous chloride SEs will pave the way to realize high-performance high-nickel cathodes for high-energy-density ASSLBs.

The collision-induced dissociation mechanism of sodiated Hex-HexNAc disaccharides.

Determining carbohydrate structures, such as their compositions, linkage positions, and in particular the anomers and stereoisomers, is a great challenge. Isomers of different anomers or stereoisomers have the same sequences of chemical bonds, but have different orientations of some chemical bonds which are difficult to be distinguished by mass spectrometry. Collision-induced dissociation (CID) tandem mass spectroscopy (MS/MS) is a widely used technique for characterizing carbohydrate structures. Understanding the carbohydrate dissociation mechanism is important for obtaining the structural information from MS/MS. In this work, we studied the CID mechanism of galactose-N-acetylgalactosamine (Gal-GalNAc) and glucose-N-acetylglucosamine (Glc-GlcNAc) disaccharides with 1→3 and 1→4 linkages. For Gal-GalNAc disaccharides, the CID mass spectra of sodium ion adducts show significant difference between the α- and ß-anomers of GalNAc at the reducing end, while no difference in the CID mass spectra between two anomers of Glc-GlcNAc disaccharides was found. Quantum chemistry calculations show that for Gal-GalNAc disaccharides, the difference of the dissociation barriers between dehydration and glycosidic bond cleavage is significantly small in the ß-anomer compared to that in the α-anomer; while these differences are similar between the α- and ß-anomers of Glc-GlcNAc disaccharides. These differences can be attributed to the different orientations of hydroxyl and N-acetyl groups located at GalNAc and GlcNAc. The calculation results are consistent with the CID spectra of isotope labelled disaccharides. Our study provides an insight into the CID of 1→3 and 1→4 linked Gal-GalNAc and Glc-GlcNAc disaccharides. This information is useful for determining the anomeric configurations of GalNAc in oligosaccharides.

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes.

Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li2ZrCl6 and Li2HfCl6, for stable ASSLMBs. The predicted halide SEs possess high Li+ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm2), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).

A Solution-Processable Porphyrin-Based Hydrogen-Bonded Organic Framework for Photoelectrochemical Sensing of Carbon Dioxide.

Detecting CO2 in complex gas mixtures is challenging due to the presence of competitive gases in the ambient atmosphere. Photoelectrochemical (PEC) techniques offer a solution, but material selection and specificity remain limiting. Here, we constructed a hydrogen-bonded organic framework material based on a porphyrin tecton decorated with diaminotriazine (DAT) moieties. The DAT moieties on the porphyrin molecules not only facilitate the formation of complementary hydrogen bonds between the tectons but also function as recognition sites in the resulting porous HOF materials for the selective adsorption of CO2 . In addition, the in-plane growth of FDU-HOF-2 into anisotropic molecular sheets with large areas of up to 23000 µm2 and controllable thickness between 0.298 and 2.407 µm were realized in yields of over 89 % by a simple solution-processing method. The FDU-HOF-2 can be directly grown and deposited onto different substrates including silica, carbon, and metal oxides by self-assembly in situ in formic acid. As a proof of concept, a screen-printing electrode deposited with FDU-HOF-2 was fabricate as a label-free photoelectrochemical (PEC) sensor for CO2 detection. Such a signal-off PEC sensor exhibits low detection limit for CO2 (2.3 ppm), reusability (at least 30 cycles), and long-term working stability (at least 30 days).

Methanol Synthesis from CO2/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search.

Methanol synthesis on industrial Cu/ZnO/Al2O3 catalysts via the hydrogenation of CO and CO2 mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO2 in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO2 and CO hydrogenations on thermodynamically favorable Cu-Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn-Zn dimeric sites being avoided. CO2 and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO2 hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [-Zn-OH-Zn-] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system.

Design and Synthesis of 6-O-Phosphorylated Heparan Sulfate Oligosaccharides to Inhibit Amyloid ß Aggregation.

Dysregulation of amyloidogenic proteins and their abnormal processing and deposition in tissues cause systemic and localized amyloidosis. Formation of amyloid ß (Aß) fibrils that deposit as amyloid plaques in Alzheimer's disease (AD) brains is an earliest pathological hallmark. The polysulfated heparan sulfate (HS)/heparin (HP) is one of the non-protein components of Aß deposits that not only modulates Aß aggregation, but also acts as a receptor for Aß fibrils to mediate their cytotoxicity. Interfering with the interaction between HS/HP and Aß could be a therapeutic strategy to arrest amyloidosis. Here we have synthesized the 6-O-phosphorylated HS/HP oligosaccharides and reported their competitive effects on the inhibition of HP-mediated Aß fibril formation in vitro using a thioflavin T fluorescence assay and a tapping mode atomic force microscopy.

A concise synthesis of l-gulose and its C-6 derivatives.

A convenient route for the preparation of l-gulose and its C-6 derivatives starting from commercially available 2,3:5,6-diisopropylidene-d-mannofuranose via C-5 epimerization as the key step was developed. 1-O-Benzylation followed by regioselective hydrolysis of the 5,6-isopropylidene group furnished benzyl 2,3-isopropylidene-α-d-mannofuranoside, which was subjected upon regioselective one-pot 6-O-benzoylation and 5-O-mesylation, providing the corresponding 5-OMs-6-OBz derivative in excellent selectivity. Treatment of this mesylate compound with potassium t-butoxide to remove the benzoyl group followed by intramolecular SN2 inversion led to benzyl 5,6-anhydro-2,3-isopropylidene-ß-l-gulofuranoside, which could undergo not only nucleophilic substitutions to open the epoxide ring to give various C-6 derivatives, but also acidic hydrolysis to yield 1,6-anhydro-ß-l-gulopyranose for further transformation into l-gulopyranosyl pentaacetate.

Byproduct formation during the biosynthesis of spinosyn A and evidence for an enzymatic interplay to prevent its formation.

Biosynthesis of spinosyn A in Saccharopolyspora spinosa involves a 1,4-dehydration followed by an intramolecular [4 + 2]-cycloaddition catalyzed by SpnM and SpnF, respectively. The cycloaddition also takes place in the absence of SpnF leading to questions regarding its mechanism of catalysis and biosynthetic role. Substrate analogs were prepared with an unactivated dienophile or an acyclic structure and found to be unreactive consistent with the importance of these features for cyclization. The SpnM-catalyzed dehydration reaction was also found to yield a byproduct corresponding to the C11 = C12 cis isomer of the SpnF substrate. This byproduct is stable both in the presence and absence of SpnF; however, relative production of the SpnM product and byproduct could be shifted in favor of the former by including SpnF or the dehydrogenase SpnJ in the reaction. This result suggests a potential interplay between the enzymes of spinosyn A biosynthesis that may help to improve the efficiency of the pathway.

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning.

The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.

In Situ Active Site for CO Activation in Fe-Catalyzed Fischer-Tropsch Synthesis from Machine Learning.

In situ-formed iron carbides (FeCx) are the key components responsible for Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) on Fe-based catalysts in industry. The true active site is, however, highly controversial despite more than a century of study, which is largely due to the combined complexity in both FeCx structures and mechanism of CO hydrogenation. Herein powered by machine learning simulation, millions of structure candidates for FeCx bulk and surfaces are explored under FTS conditions, which leads to resolving the active site for CO activation. This is achieved without a priori input from experiment by first constructing the thermodynamics convex hull of bulk phases, followed by identifying the low surface energy surfaces and evaluating the adsorption ability of CO and H, and finally determining the lowest energy reaction pathway of CO activation. Rich information on FeCx structures and CO hydrogenation pathways is gleaned: (i) Fe5C2, Fe7C3, and Fe2C are the three stable bulk phases under FTS in producing olefins, where Fe7C3 and Fe2C have multiple energetically nearly degenerate bulk crystal phases; (ii) only three low surface energy surfaces of these bulk phases, namely, χ-Fe5C2(510), χ-Fe5C2(111), and η-Fe2C(111), expose the Fe sites that can adsorb H atoms exothermically, where the surface Fe:C ratio is 2, 1.75, and 2, respectively; (iii) CO activation via direct dissociation can occur at the surface C vacancies (e.g., with a barrier of 1.1 eV) that are created dynamically via hydrogenation. These atomic-level understandings facilitate the building of the structure-activity correlation and designing better FT catalysts.

In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment.

PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO2 supported Pd1Ag3 catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd1Ag1 (R3̅m) and Pd1Ag3 (Pm3̅m), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets.

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration.

Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.The recent advances of machine-learning (ML)-based large-scale atomic simulations has shown great promise to the benefit of many branches of chemistry. Instead of solving the Schrödinger equation directly, ML-based simulations rely on a large data set of accurate potential energy surfaces (PESs) and complex numerical models to predict the total energy. These simulations feature both a high speed and a high accuracy for computing large systems. Due to the lack of a physical foundation in numerical models, ML models are often frustrated in their predictivity and robustness, which are key to applications. Focusing on these concerns, here we overview the recent advances in ML methodologies for atomic simulations on three key aspects. Namely, the generation of a representative data set, the extensity of ML models, and the continuity of data representation. While global optimization methods are the natural choice for building a representative data set, the stochastic surface walking method is shown to provide the desired PES sampling for both minima and transition regions on the PES. The current ML models generally utilize local geometrical descriptors as an input and consider the total energy as the sum of atomic energies. There are many flavors of data descriptors and ML models, but the applications for material and reaction predictions are still limited, not least because of the difficulty to train the associated vast global data sets. We show that our recently designed power-type structure descriptors together with a feed-forward neural network (NN) model are compatible with highly complex global PES data, which has led to a large family of global NN (G-NN) potentials.Two recent applications of G-NN potentials in material and reaction simulations are selected to illustrate how ML-based atomic simulations can help the discovery of new materials and reactions.

Glycan sulfation patterns define autophagy flux at axon tip via PTPRσ-cortactin axis.

Chondroitin sulfate (CS) and heparan sulfate (HS) are glycosaminoglycans that both bind the receptor-type protein tyrosine phosphatase PTPRσ, affecting axonal regeneration. CS inhibits axonal growth, while HS promotes it. Here, we have prepared a library of HS octasaccharides and, together with synthetic CS oligomers, we found that PTPRσ preferentially interacts with CS-E-a rare sulfation pattern in natural CS-and most HS oligomers bearing sulfate and sulfamate groups. Consequently, short and long stretches of natural CS and HS, respectively, bind to PTPRσ. CS activates PTPRσ, which dephosphorylates cortactin-herein identified as a new PTPRσ substrate-and disrupts autophagy flux at the autophagosome-lysosome fusion step. Such disruption is required and sufficient for dystrophic endball formation and inhibition of axonal regeneration. Therefore, sulfation patterns determine the length of the glycosaminoglycan segment that bind to PTPRσ and define the fate of axonal regeneration through a mechanism involving PTPRσ, cortactin and autophagy.

Practical Remdesivir Synthesis through One-Pot Organocatalyzed Asymmetric (S)-P-Phosphoramidation.

Remdesivir, an inhibitor of RNA-dependent RNA polymerase developed by Gilead Sciences, has been used for the treatment of COVID-19. The synthesis of remdesivir is, however, challenging, and the overall cost is relatively high. Particularly, the stereoselective assembly of the P-chirogenic center requires recrystallization of a 1:1 isomeric p-nitrophenylphosphoramidate mixture several times to obtain the desired diastereoisomer (39%) for further coupling with the d-ribose-derived 5-alcohol. To address this problem, a variety of chiral bicyclic imidazoles were synthesized as organocatalysts for stereoselective (S)-P-phosphoramidation employing a 1:1 diastereomeric mixture of phosphoramidoyl chloridates as the coupling reagent to avoid a waste of the other diastereomer. Through a systematic study of different catalysts at different temperatures and concentrations, a mixture of the (S)- and (R)-P-phosphoramidates was obtained in 97% yield with a 96.1/3.9 ratio when 20 mol % of the chiral imidazole-cinnamaldehyde-derived carbamate was utilized in the reaction at -20 °C. A 10-g scale one-pot synthesis via a combination of (S)-P-phosphoramidation and protecting group removal followed by one-step recrystallization gave remdesivir in 70% yield and 99.3/0.7 d.r. The organocatalyst was recovered in 83% yield for reuse, and similar results were obtained. This one-pot process offers an excellent opportunity for industrial production of remdesivir.

[Prostatitis studies in China from 2000 to 2020: A Knowledge Atlas-based study on the development trend of contemporary disciplines].

OBJECTIVE: To review and analyze the trend of researches on prostatitis in China in the past two decades. METHODS: We searched the core collection of China National Knowledge Infrastructure (CNKI) for studies on prostatitis, and analyzed the data obtained using Excel, Citespace and VOSviewer. RESULTS: Totally, 1 216 original articles were identified, with 3 271 keywords, ≥3-time high-frequency keywords accounting for 12.9%, with "", "", "chronic prostatitis", "prostatitis", and "" as the top 5 ones, each with a centrality higher than 300. Major prostatitis-related studies focused on the 8 keywords, namely, prostatitis, prostatic fluid, rats, prostate, syndromes, efficacy observation, compound (in traditional Chinese medicine, TCM), and therapeutic application. The included literature involved 2 808 authors, with 402 involved more than twice and most of them in a scattered manner. The major topics of prostatitis studies varied in the past two decades, focusing on TCM therapies, promotion of blood circulation and stasis and comprehensive nursing in 2000－2001, on animal models, CD4+ lymphocytes and other experimental molecules in 2007－2010, on urodynamics, risk factors and specific antigens in 2013－2016, and on literature information resources in 2016. CONCLUSIONS: The immune mechanism remains a hot topic in the future researches on prostatitis. In terms of treatment of the disease, TCM has a potential value, and more practice and studies are required for an optimal combination of TCM and Western medicine. Strengthened collaborative efforts are needed to establish an authoritative source channel for the keywords, and incorporate it into the national standard system, and above all, to integrate the prostatitis study into multi-disciplinary researches, eliminate academic barriers, encourage collaborative innovation with multiple parties, and promote the exchanges and development in this field.

Trisaccharide Sulfate and Its Sulfonamide as an Effective Substrate and Inhibitor of Human Endo-O-sulfatase-1.

Human endo-O-sulfatases (Sulf-1 and Sulf-2) are extracellular heparan sulfate proteoglycan (HSPG)-specific 6-O-endosulfatases, which regulate a multitude of cell-signaling events through heparan sulfate (HS)-protein interactions and are associated with the onset of osteoarthritis. These endo-O-sulfatases are transported onto the cell surface to liberate the 6-sulfate groups from the internal d-glucosamine residues in the highly sulfated subdomains of HSPGs. In this study, a variety of HS oligosaccharides with different chain lengths and N- and O-sulfation patterns via chemical synthesis were systematically studied about the substrate specificity of human Sulf-1 employing the fluorogenic substrate 4-methylumbelliferyl sulfate (4-MUS) in a competition assay. The trisaccharide sulfate IdoA2S-GlcNS6S-IdoA2S was found to be the minimal-size substrate for Sulf-1, and substitution of the sulfate group at the 6-O position of the d-glucosamine unit with the sulfonamide motif effectively inhibited the Sulf-1 activity with IC50 = 0.53 µM, Ki = 0.36 µM, and KD = 12 nM.

The Accumulation of Heparan Sulfate S-Domains in Kidney Transthyretin Deposits Accelerates Fibril Formation and Promotes Cytotoxicity.

The highly sulfated domains of heparan sulfate (HS), alias HS S-domains, are made up of repeated trisulfated disaccharide units [iduronic acid (2S)-glucosamine (NS, 6S)] and are selectively remodeled by extracellular endoglucosamine 6-sulfatases (Sulfs). Although HS S-domains are critical for signal transduction of several growth factors, their roles in amyloidoses are not yet fully understood. Herein, we found HS S-domains in the kidney of a patient with transthyretin amyloidosis. In in vitro assays with cells stably expressing human Sulfs, heparin, a structural analog of HS S-domains, promoted aggregation of transthyretin in an HS S-domain-dependent manner. Interactions of cells with transthyretin fibrils and cytotoxicity of these fibrils also depended on HS S-domains at the cell surface. Furthermore, glypican-5, encoded by the susceptibility gene for nephrotic syndrome GPC5, was found to be accumulated in the transthyretin amyloidosis kidney. Our study, thus, provides a novel insight into the pathologic roles of HS S-domains in amyloidoses, and we propose that enzymatic remodeling of HS chains by Sulfs may offer an effective approach to inhibiting formation and cytotoxicity of amyloid fibrils.