hnRNPAB inhibits Influenza A virus infection by disturbing polymerase activity.

Influenza A virus (IAV) continuously poses a considerable threat to global health through seasonal epidemics and recurring pandemics. IAV RNA-dependent RNA polymerases (FluPol) mediate the transcription of RNA and replication of the viral genome. Searching for targets that inhibit viral polymerase activity helps us develop better antiviral drugs. Here, we identified heterogeneous nuclear ribonucleoprotein A/B (hnRNPAB) as an anti-influenza host factor. hnRNPAB interacts with NP of IAV to inhibit the interaction between PB1 and NP, which is dependent on the 5-amino-acid peptide of the hnRNPAB C-terminal domain (aa 318-322). We further found that the 5-amino-acid peptide blocks the interaction between PB1 and NP to destroy the FluPol activity. In vivo studies demonstrate that hnRNPAB-deficient mice display higher viral burdens, enhanced cytokine production, and increased mortality after influenza infection. These data demonstrate that hnRNPAB perturbs FluPol complex conformation to inhibit IAV infection, providing insights into anti-influenza defense mechanisms.

Long-term effects of Omicron BA.2 breakthrough infection on immunity-metabolism balance: a 6-month prospective study.

There have been reports of long coronavirus disease (long COVID) and breakthrough infections (BTIs); however, the mechanisms and pathological features of long COVID after Omicron BTIs remain unclear. Assessing long-term effects of COVID-19 and immune recovery after Omicron BTIs is crucial for understanding the disease and managing new-generation vaccines. Here, we followed up mild BA.2 BTI convalescents for six-month with routine blood tests, proteomic analysis and single-cell RNA sequencing (scRNA-seq). We found that major organs exhibited ephemeral dysfunction and recovered to normal in approximately six-month after BA.2 BTI. We also observed durable and potent levels of neutralizing antibodies against major circulating sub-variants, indicating that hybrid humoral immunity stays active. However, platelets may take longer to recover based on proteomic analyses, which also shows coagulation disorder and an imbalance between anti-pathogen immunity and metabolism six-month after BA.2 BTI. The immunity-metabolism imbalance was then confirmed with retrospective analysis of abnormal levels of hormones, low blood glucose level and coagulation profile. The long-term malfunctional coagulation and imbalance in the material metabolism and immunity may contribute to the development of long COVID and act as useful indicator for assessing recovery and the long-term impacts after Omicron sub-variant BTIs.

Transaldolase inhibits CD36 expression by modulating glutathione-p38 signaling, exerting protective effects against macrophage foam cell formation.

In atherosclerosis, macrophage-derived foam cell formation is considered to be a hallmark of the pathological process; this occurs via the uptake of modified lipoproteins. In the present study, we aim to determine the role of transaldolase in foam cell formation and atherogenesis and reveal the mechanisms underlying its role. Bone marrow-derived macrophages (BMDMs) isolated from mice successfully form foam cells after treatment with oxidized low-density lipoprotein (80 µg/mL). Elevated transaldolase levels in the foam cell model are assessed by quantitative polymerase chain reaction and western blot analysis. Transaldolase overexpression and knockdown in BMDMs are achieved via plasmid transfection and small interfering RNA technology, respectively. We find that transaldolase overexpression effectively attenuates, whereas transaldolase knockdown accelerates, macrophage-derived foam cell formation through the inhibition or activation of cholesterol uptake mediated by the scavenger receptor cluster of differentiation 36 (CD36) in a p38 mitogen-activated protein kinase (MAPK) signaling-dependent manner. Transaldolase-mediated glutathione (GSH) homeostasis is identified as the upstream regulator of p38 MAPK-mediated CD36-dependent cholesterol uptake in BMDMs. Transaldolase upregulates GSH production, thereby suppressing p38 activity and reducing the CD36 level, ultimately preventing foam cell formation and atherosclerosis. Thus, our findings indicate that the transaldolase-GSH-p38-CD36 axis may represent a promising therapeutic target for atherosclerosis.

Data-driven prediction of complex crystal structures of dense lithium.

Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine-learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal structures containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine learning potentials.

Stabilization of S3O4 at high pressure: implications for the sulfur-excess paradox.

The amount of sulfur in SO2 discharged in volcanic eruptions exceeds that available for degassing from the erupted magma. This geological conundrum, known as the "sulfur excess", has been the subject of considerable interests but remains an open question. Here, in a systematic computational investigation of sulfur-oxygen compounds under pressure, a hitherto unknown S3O4 compound containing a mixture of sulfur oxidation states +II and +IV is predicted to be stable at pressures above 79 GPa. We speculate that S3O4 may be produced via redox reactions involving subducted S-bearing minerals (e.g., sulfates and sulfides) with iron and goethite under high-pressure conditions of the deep lower mantle, decomposing to SO2 and S at shallow depths. S3O4 may thus be a key intermediate in promoting decomposition of sulfates to release SO2, offering an alternative source of excess sulfur released during explosive eruptions. These findings provide a possible resolution of the "excess sulfur degassing" paradox and a viable mechanism for the exchange of S between Earth's surface and the lower mantle in the deep sulfur cycle.

Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface.

The crystal structure prediction (CSP) has emerged in recent years as a major theme in research across many scientific disciplines in physics, chemistry, materials science, and geoscience, among others. The central task here is to find the global energy minimum on the potential energy surface (PES) associated with the vast structural configuration space of pertinent crystals of interest, which presents a formidable challenge to efficient and reliable computational implementation. Considerable progress in recent CSP algorithm developments has led to many methodological advances along with successful applications, ushering in a new paradigm where computational research plays a leading predictive role in finding novel material forms and properties which, in turn, offer key insights to guide experimental synthesis and characterization. In this Account, we first present a concise summary of major advances in various CSP methods, with an emphasis on the overarching fundamentals for the exploration of the PES and its impact on CSP. We then take our developed CALYPSO method as an exemplary case study to give a focused overview of the current status of the most prominent issues in CSP methodology. We also provide an overview of the basic theory and main features of CALYPSO and emphasize several effective strategies in the CALYPSO methodology to achieve a good balance between exploration and exploitation. We showcase two exemplary cases of the theory-driven discovery of high-temperature superconducting superhydrides and a select group of atypical compounds, where CSP plays a significant role in guiding experimental synthesis toward the discovery of new materials. We finally conclude by offering perspectives on major outstanding issues and promising opportunities for further CSP research.

Disproportionation of SO_{2} at High Pressure and Temperature.

Materials once suffered at high-pressure and high-temperature (HPHT) conditions often exhibit exotic phenomena that defy conventional wisdom. The behaviors of sulfur dioxide (SO_{2}), one of the archetypal simple molecules, at HPHT conditions have attracted a great deal of attention due to its relevance to the S cycle between deep Earth and the atmosphere. Here we report the discovery of an unexpected disproportionation of SO_{2} via bond breaking into elemental S and sulfur trioxide (SO_{3}) at HPHT conditions through a jointly experimental and theoretical study. Measured x-ray diffraction and Raman spectroscopy data allow us to solve unambiguously the crystal structure (space group R3[over ¯]c) of the resultant SO_{3} phase that shows an extended framework structure formed by vertex-sharing octahedra SO_{6}. Our findings lead to a significant extension of the phase diagram of SO_{2} and suggest that SO_{2}, despite its abundance in Earth's atmosphere and ubiquity in other giant planets, is not a stable compound at HPHT conditions relevant to planetary interiors, providing important implications for elucidating the S chemistry in deep Earth and other giant planets.

A symmetry-orientated divide-and-conquer method for crystal structure prediction.

Crystal structure prediction has been a subject of topical interest but remains a substantial challenge especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy surface. In this paper, a symmetry-orientated divide-and-conquer scheme was proposed to construct a symmetry tree graph, where the entire search space is decomposed into a finite number of symmetry dependent subspaces. An artificial intelligence-based symmetry selection strategy was subsequently devised to select the low-lying subspaces with high symmetries for global exploration and in-depth exploitation. Our approach can significantly simplify the problem of crystal structure prediction by avoiding exploration of the most complex P1 subspace on the entire search space and has the advantage of preserving the crystal symmetry during structure evolution, making it well suitable for predicting the complex crystal structures. The effectiveness of the method has been validated by successful prediction of the candidate structures of binary Lennard-Jones mixtures and the high-pressure phase of ice, containing more than 100 atoms in the simulation cell. The work therefore opens up an opportunity toward achieving the long-sought goal of crystal structure prediction of complex systems.

Pressure-stabilized high-energy-density material YN10.

Polynitrogen compounds have been intensively studied for potential applications as high energy density materials, especially in energy and military fields. Here, using the swarm intelligence algorithm in combination with first-principles calculations, we systematically explored the variable stoichiometries of yttrium-nitrogen compounds on the nitrogen-rich regime at high pressure, where a new stable phase of YN10adoptingI4/msymmetry was discovered at the pressure of 35 GPa and showed metallic character from the analysis of electronic properties. In YN10, all the nitrogen atoms weresp2-hybridized in the form of N5ring. Furthermore, the gravimetric and volumetric energy densities were estimated to be 3.05 kJ g-1and 9.27 kJ cm-1respectively. Particularly, the calculated detonation velocity and pressure of YN10(12.0 km s-1, 82.7 GPa) was higher than that of TNT (6.9 km s-1, 19.0 GPa) and HMX (9.1 km s-1, 39.3 GPa), making it a potential candidate as a high-energy-density material.

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery.

The theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), based solely on chemical composition, has already become a routine tool to determine the structures of physical and chemical systems, e.g., solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded because of the unfavorable scaling of the computational cost with respect to the system size. During recent years, the machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction to the basic principles and advantages of the combination of structure prediction and MLP, as well as the challenges and opportunities associated with this promising approach.

Ab initio electronic structure calculations using a real-space Chebyshev-filtered subspace iteration method.

Ab initio electronic structure calculations within Kohn-Sham density functional theory requires a solution for the Kohn-Sham equation. However, the traditional self-consistent field (SCF) approach of solving the equation using iterative diagonalization exhibits an inherent cubic scaling behavior and becomes prohibitive for large systems. The Chebyshev-filtered subspace iteration (CheFSI) method holds considerable promise for large-system calculations by substantially accelerating the SCF procedure. Here, we employed a combination of the real space finite-difference formulation and CheFSI to solve the Kohn-Sham equation, and implemented this approach in ab initio Real-space Electronic Structure (ARES) software in a multi-processor, parallel environment. An improved scheme was proposed to generate the initial subspace of Chebyshev filtering in ARES efficiently, making it suitable for large-scale simulations. The accuracy, stability, and efficiency of the ARES software were illustrated by simulations of large-scale crystalline systems containing thousands of atoms.

Interface structure prediction via CALYPSO method.

The atomistic structures of solid-solid interfaces are of fundamental interests for understanding physical properties of interfacial materials. However, determination of interface structures faces a substantial challenge, both experimentally and theoretically. Here, we propose an efficient method for predicting interface structures via the generalization of our in-house developed CALYPSO method for structure prediction. We devised a lattice match toolkit that allows us to automatically search for the optimal lattice-matched superlattice for construction of the interface structures. In addition, bonding constraints (e.g., constraints on interatomic distances and coordination numbers of atoms) are imposed to generate better starting interface structures by taking advantages of the known bonding environment derived from the stable bulk phases. The interface structures evolve by following interfacially confined swarm intelligence algorithm, which is known to be efficient for exploration of potential energy surface. The method was validated by correctly predicting a number of known interface structures with only given information of two parent solids. The application of the developed method leads to prediction of two unknown grain boundary (GB) structures (r-GB and p-GB) of rutile TiO2 Σ5(2 1 0) under an O reducing atmosphere that contained Ti3+ as the result of O defects. Further calculations revealed that the intrinsic band gap of p-GB is reduced to 0.7 eV owing to substantial broadening of the Ti-3d interfacial levels from Ti3+ centers. Our results demonstrated that introduction of grain boundaries is an effective strategy to engineer the electronic properties and thus enhance the visible-light photoactivity of TiO2.