Observation of plaid-like spin splitting in a noncoplanar antiferromagnet.

Spatial, momentum and energy separation of electronic spins in condensed-matter systems guides the development of new devices in which spin-polarized current is generated and manipulated1-3. Recent attention on a set of previously overlooked symmetry operations in magnetic materials4 leads to the emergence of a new type of spin splitting, enabling giant and momentum-dependent spin polarization of energy bands on selected antiferromagnets5-10. Despite the ever-growing theoretical predictions, the direct spectroscopic proof of such spin splitting is still lacking. Here we provide solid spectroscopic and computational evidence for the existence of such materials. In the noncoplanar antiferromagnet manganese ditelluride (MnTe2), the in-plane components of spin are found to be antisymmetric about the high-symmetry planes of the Brillouin zone, comprising a plaid-like spin texture in the antiferromagnetic (AFM) ground state. Such an unconventional spin pattern, further found to diminish at the high-temperature paramagnetic state, originates from the intrinsic AFM order instead of spin-orbit coupling (SOC). Our finding demonstrates a new type of quadratic spin texture induced by time-reversal breaking, placing AFM spintronics on a firm basis and paving the way for studying exotic quantum phenomena in related materials.

Molecular mechanisms of SARS-CoV-2 resistance to nirmatrelvir.

Nirmatrelvir is a specific antiviral drug that targets the main protease (Mpro) of SARS-CoV-2 and has been approved to treat COVID-191,2. As an RNA virus characterized by high mutation rates, whether SARS-CoV-2 will develop resistance to nirmatrelvir is a question of concern. Our previous studies have shown that several mutational pathways confer resistance to nirmatrelvir, but some result in a loss of viral replicative fitness, which is then compensated for by additional alterations3. The molecular mechanisms for this observed resistance are unknown. Here we combined biochemical and structural methods to demonstrate that alterations at the substrate-binding pocket of Mpro can allow SARS-CoV-2 to develop resistance to nirmatrelvir in two distinct ways. Comprehensive studies of the structures of 14 Mpro mutants in complex with drugs or substrate revealed that alterations at the S1 and S4 subsites substantially decreased the level of inhibitor binding, whereas alterations at the S2 and S4' subsites unexpectedly increased protease activity. Both mechanisms contributed to nirmatrelvir resistance, with the latter compensating for the loss in enzymatic activity of the former, which in turn accounted for the restoration of viral replicative fitness, as observed previously3. Such a profile was also observed for ensitrelvir, another clinically relevant Mpro inhibitor. These results shed light on the mechanisms by which SARS-CoV-2 evolves to develop resistance to the current generation of protease inhibitors and provide the basis for the design of next-generation Mpro inhibitors.

Hypernetwork modeling and topology of high-order interactions for complex systems.

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

xSiGra: explainable model for single-cell spatial data elucidation.

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.

The DNA loop release factor WAPL suppresses Epstein-Barr virus latent membrane protein expression to maintain the highly restricted latency I program.

Epstein-Barr virus (EBV) uses latency programs to colonize the memory B-cell reservoir, and each program is associated with human malignancies. However, knowledge remains incomplete of epigenetic mechanisms that maintain the highly restricted latency I program, present in memory and Burkitt lymphoma cells, in which EBNA1 is the only EBV-encoded protein expressed. Given increasing appreciation that higher order chromatin architecture is an important determinant of viral and host gene expression, we investigated roles of Wings Apart-Like Protein Homolog (WAPL), a host factor that unloads cohesin to control DNA loop size and that was discovered as an EBNA2-associated protein. WAPL knockout (KO) in Burkitt cells de-repressed LMP1 and LMP2A expression, but not other EBV oncogenes, to yield a viral program reminiscent of EBV latency II, which is rarely observed in B-cells. WAPL KO also increased LMP1/2A levels in latency III lymphoblastoid cells. WAPL KO altered EBV genome architecture, triggering formation of DNA loops between the LMP promoter region and the EBV origins of lytic replication (oriLyt). Hi-C analysis further demonstrated that WAPL KO reprogrammed EBV genomic DNA looping. LMP1 and LMP2A de-repression correlated with decreased histone repressive marks at their promoters. We propose that EBV coopts WAPL to negatively regulate latent membrane protein expression to maintain Burkitt latency I.

Structural and biochemical characterization of the key components of an auxin degradation operon from the rhizosphere bacterium Variovorax.

Plant-associated bacteria play important regulatory roles in modulating plant hormone auxin levels, affecting the growth and yields of crops. A conserved auxin degradation (iad) operon was recently identified in the Variovorax genomes, which is responsible for root growth inhibition (RGI) reversion, promoting rhizosphere colonization and root growth. However, the molecular mechanism underlying auxin degradation by Variovorax remains unclear. Here, we systematically screened Variovorax iad operon products and identified 2 proteins, IadK2 and IadD, that directly associate with auxin indole-3-acetic acid (IAA). Further biochemical and structural studies revealed that IadK2 is a highly IAA-specific ATP-binding cassette (ABC) transporter solute-binding protein (SBP), likely involved in IAA uptake. IadD interacts with IadE to form a functional Rieske non-heme dioxygenase, which works in concert with a FMN-type reductase encoded by gene iadC to transform IAA into the biologically inactive 2-oxindole-3-acetic acid (oxIAA), representing a new bacterial pathway for IAA inactivation/degradation. Importantly, incorporation of a minimum set of iadC/D/E genes could enable IAA transformation by Escherichia coli, suggesting a promising strategy for repurposing the iad operon for IAA regulation. Together, our study identifies the key components and underlying mechanisms involved in IAA transformation by Variovorax and brings new insights into the bacterial turnover of plant hormones, which would provide the basis for potential applications in rhizosphere optimization and ecological agriculture.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors.

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019-2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)1-4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (Mpro) of SARS-CoV-2: Mpro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-25,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of Mpro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds-including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds-as inhibitors of Mpro. Six of these compounds inhibited Mpro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 µM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available.

Structural basis for the concerted antiphage activity in the SIR2-HerA system.

Recently, a novel two-gene bacterial defense system against phages, encoding a SIR2 NADase and a HerA ATPase/helicase, has been identified. However, the molecular mechanism of the bacterial SIR2-HerA immune system remains unclear. Here, we determine the cryo-EM structures of SIR2, HerA and their complex from Paenibacillus sp. 453MF in different functional states. The SIR2 proteins oligomerize into a dodecameric ring-shaped structure consisting of two layers of interlocked hexamers, in which each subunit exhibits an auto-inhibited conformation. Distinct from the canonical AAA+ proteins, HerA hexamer alone in this antiphage system adopts a split spiral arrangement, which is stabilized by a unique C-terminal extension. SIR2 and HerA proteins assemble into a ∼1.1 MDa torch-shaped complex to fight against phage infection. Importantly, disruption of the interactions between SIR2 and HerA largely abolishes the antiphage activity. Interestingly, binding alters the oligomer state of SIR2, switching from a dodecamer to a tetradecamer state. The formation of the SIR2-HerA binary complex activates NADase and nuclease activities in SIR2 and ATPase and helicase activities in HerA. Together, our study not only provides a structural basis for the functional communications between SIR2 and HerA proteins, but also unravels a novel concerted antiviral mechanism through NAD+ degradation, ATP hydrolysis, and DNA cleavage.

N-doped carbon-iron heterointerfaces for boosted electrocatalytic active and selective ammonia production.

The electrochemical conversion of waste nitrate (NO3-) to valuable ammonia (NH3) is an economical and environmentally friendly technology for sustainable NH3 production. It is beneficial for environmental nitrogen pollution management and is also an appealing alternative to the current Haber-Bosch process for NH3 production. However, owing to the competing hydrogen evolution reaction, it is necessary to design highly efficient and stable electrocatalysts with high selectivity. Herein, we report a rational design of Fe nanoparticles wrapped in N-doped carbon (Fe@N10-C) as a high NH3 selective and efficient electrocatalyst using a metal-organic framework precursor. We constructed a catalyst with new active sites by doping with nitrogen, which activated neighboring carbon atoms and enhanced metal-to-carbon electron transfer, resulting in high catalytic activity. These doped N sites play a key role in the NO3- electroreduction. As a result, the Fe@N10-C nanoparticles with optimal doping of N demonstrated remarkable performance, with a record-high NO3- removal capacity of 125.8 ± 0.5 mg N gcat-1 h-1 and nearly 100 % (99.7 ± 0.1%) selectivity. The catalyst also delivers an impressive NH3 production rate of 2647.7 µg h-1 cm-2 and high faradaic efficiency of 91.8 ± 0.1%. This work provides a new route for N-doped carbon-iron catalysis application and paves the way for addressing energy and environmental issues.

The metabolomic physics of complex diseases.

Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.

A spatiotemporal barrier formed by Follistatin is required for left-right patterning.

How left-right (LR) asymmetry emerges in a patterning field along the anterior-posterior axis remains an unresolved problem in developmental biology. Left-biased Nodal emanating from the LR organizer propagates from posterior to anterior (PA) and establishes the LR pattern of the whole embryo. However, little is known about the regulatory mechanism of the PA spread of Nodal and its asymmetric activation in the forebrain. Here, we identify bilaterally expressed Follistatin (Fst) as a regulator blocking the propagation of the zebrafish Nodal ortholog Southpaw (Spaw) in the right lateral plate mesoderm (LPM), and restricting Spaw transmission in the left LPM to facilitate the establishment of a robust LR asymmetric Nodal patterning. In addition, Fst inhibits the Activin-Nodal signaling pathway in the forebrain thus preventing Nodal activation prior to the arrival, at a later time, of Spaw emanating from the left LPM. This contributes to the orderly propagation of asymmetric Nodal activation along the PA axis. The LR regulation function of Fst is further confirmed in chick and frog embryos. Overall, our results suggest that a robust LR patterning emerges by counteracting a Fst barrier formed along the PA axis.

Gut microbiota-derived butyrate promotes coronavirus TGEV infection through impairing RIG-I-triggered local type I interferon responses via class I HDAC inhibition.

Gut microbiota-derived metabolites are important for the replication and pathogenesis of many viruses. However, the roles of bacterial metabolites in swine enteric coronavirus (SECoV) infection remain poorly understood. Recent studies show that SECoVs infection in vivo significantly alters the composition of short-chain fatty acids (SCFAs)-producing gut microbiota. This prompted us to investigate whether and how SCFAs impact SECoV infection. Employing alphacoronavirus transmissible gastroenteritis virus (TGEV), a major cause of diarrhea in piglets, as a model, we found that SCFAs, particularly butyrate, enhanced TGEV infection both in porcine intestinal epithelial cells and swine testicular (ST) cells at the late stage of viral infection. This effect depended on the inhibited productions of virus-induced type I interferon (IFN) and downstream antiviral IFN-stimulated genes (ISGs) by butyrate. Mechanistically, butyrate suppressed the expression of retinoic acid-inducible gene I (RIG-I), a key viral RNA sensor, and downstream mitochondrial antiviral-signaling (MAVS) aggregation, thereby impairing type I IFN responses and increasing TGEV replication. Using pharmacological and genetic approaches, we showed that butyrate inhibited RIG-I-induced type I IFN signaling by suppressing class I histone deacetylase (HDAC). In summary, we identified a novel mechanism where butyrate enhances TGEV infection by suppressing RIG-I-mediated type I IFN responses. Our findings highlight that gut microbiota-derived metabolites like butyrate can be exploited by SECoV to dampen innate antiviral immunity and establish infection in the intestine.IMPORTANCESwine enteric coronaviruses (SECoVs) infection in vivo alters the composition of short-chain fatty acids (SCFAs)-producing gut microbiota, but whether microbiota-derived SCFAs impact coronavirus gastrointestinal infection is largely unknown. Here, we demonstrated that SCFAs, particularly butyrate, substantially increased alphacoronavirus TGEV infection at the late stage of infection, without affecting viral attachment or internalization. Furthermore, enhancement of TGEV by butyrate depended on impeding virus-induced type I interferon (IFN) responses. Mechanistically, butyrate suppressed the cytoplasmic viral RNA sensor RIG-I expression and downstream type I IFN signaling activation by inhibiting class I HDAC, thereby promoting TGEV infection. Our work reveals novel functions of gut microbiota-derived SCFAs in enhancing enteric coronavirus infection by impairing RIG-I-dependent type I IFN responses. This implies that bacterial metabolites could be therapeutic targets against SECoV infection by modulating antiviral immunity in the intestine.

PEDV evades MHC-I-related immunity through nsp1-mediated NLRC5 translation inhibition.

Major histocompatibility complex class I (MHC-I) plays crucial roles against viral infections not only by initiating CD8+ T cell immunity but also by modulating natural killer (NK) cell cytotoxicity. Understanding how viruses precisely regulate MHC-I to optimize their infection is important; however, the manipulation of MHC-I molecules by porcine epidemic diarrhea virus (PEDV) remains unclear. In this study, we demonstrate that PEDV infection promotes the transcription of NLRC5, a key transactivator of MHC-I, in several porcine cell lines and in vivo. Paradoxically, no increase in MHC-I expression is observed after PEDV infection both in vitro and in vivo. Mechanistic studies revealed that PEDV infection inhibits the translation of PEDV-elicited NLRC5 mRNA and the expression of downstream MHC-I proteins, without affecting the expression of physiological NLRC5 and MHC-I proteins. Through viral protein screening, we identified PEDV nonstructural protein 1 (nsp1) as the critical antagonist that inhibits NLRC5-mediated upregulation of MHC-I, and the nsp1's inhibitory effect on MHC-I requires the motif of 15 amino acids at its C-terminus. Notably, our results revealed that the cytotoxic ability of NK cells against PEDV-infected cells is similar to that against healthy cells. Collectively, our findings uncover an immune evasion mechanism by which PEDV-infected cells masquerade as healthy cells to evade NK and T cell immunity. This is achieved by targeting NLRC5, a key MHC-I transcriptional regulator, via nsp1.IMPORTANCEPorcine epidemic diarrhea virus (PEDV) is a highly contagious enteric coronavirus that inflicts substantial financial losses on the swine industry. Major histocompatibility complex class I (MHC-I) is a critical factor influencing both CD8+ T cell and natural killer (NK) cell immunity. However, how PEDV manipulates MHC-I expression to optimize its infection process remains largely unknown. In this study, we demonstrate that PEDV's nonstructural protein 1 (nsp1) inhibits virus-mediated induction of MHC-I expression by directly targeting NLRC5, a key MHC-I transactivator. Intriguingly, nsp1 does not reduce physiological NLRC5 and MHC-I expression. This selective inhibition of virus-elicited NLRC5 mRNA translation allows PEDV-infected cells to masquerade as healthy cells, thereby evading CD8+ T cell and NK cell cytotoxicity. Our findings provide unique insights into the mechanisms by which PEDV evades CD8+ T cell and NK cell immunity.

Persistent Tor-algebra for protein-protein interaction analysis.

Protein-protein interactions (PPIs) play crucial roles in almost all biological processes from cell-signaling and membrane transport to metabolism and immune systems. Efficient characterization of PPIs at the molecular level is key to the fundamental understanding of PPI mechanisms. Even with the gigantic amount of PPI models from graphs, networks, geometry and topology, it remains as a great challenge to design functional models that efficiently characterize the complicated multiphysical information within PPIs. Here we propose persistent Tor-algebra (PTA) model for a unified algebraic representation of the multiphysical interactions. Mathematically, our PTA is inherently algebraic data analysis. In our PTA model, protein structures and interactions are described as a series of face rings and Tor modules, from which PTA model is developed. The multiphysical information within/between biomolecules are implicitly characterized by PTA and further represented as PTA barcodes. To test our PTA models, we consider PTA-based ensemble learning for PPI binding affinity prediction. The two most commonly used datasets, i.e. SKEMPI and AB-Bind, are employed. It has been found that our model outperforms all the existing models as far as we know. Mathematically, our PTA model provides a highly efficient way for the characterization of molecular structures and interactions.

SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.

Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.

Mycn regulates intestinal development through ribosomal biogenesis in a zebrafish model of Feingold syndrome 1.

Feingold syndrome type 1, caused by loss-of-function of MYCN, is characterized by varied phenotypes including esophageal and duodenal atresia. However, no adequate model exists for studying the syndrome's pathological or molecular mechanisms, nor is there a treatment strategy. Here, we developed a zebrafish Feingold syndrome type 1 model with nonfunctional mycn, which had severe intestinal atresia. Single-cell RNA-seq identified a subcluster of intestinal cells that were highly sensitive to Mycn, and impaired cell proliferation decreased the overall number of intestinal cells in the mycn mutant fish. Bulk RNA-seq and metabolomic analysis showed that expression of ribosomal genes was down-regulated and that amino acid metabolism was abnormal. Northern blot and ribosomal profiling analysis showed abnormal rRNA processing and decreases in free 40S, 60S, and 80S ribosome particles, which led to impaired translation in the mutant. Besides, both Ribo-seq and western blot analysis showed that mTOR pathway was impaired in mycn mutant, and blocking mTOR pathway by rapamycin treatment can mimic the intestinal defect, and both L-leucine and Rheb, which can elevate translation via activating TOR pathway, could rescue the intestinal phenotype of mycn mutant. In summary, by this zebrafish Feingold syndrome type 1 model, we found that disturbance of ribosomal biogenesis and blockage of protein synthesis during development are primary causes of the intestinal defect in Feingold syndrome type 1. Importantly, our work suggests that leucine supplementation may be a feasible and easy treatment option for this disease.

An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

High-ammonia selective metal-organic framework-derived Co-doped Fe/Fe2O3 catalysts for electrochemical nitrate reduction.

Ammonia (NH3) is an ideal carbon-free power source in the future sustainable hydrogen economy for growing energy demand. The electrochemical nitrate reduction reaction (NO3-RR) is a promising approach for nitrate removal and NH3 production at ambient conditions, but efficient electrocatalysts are lacking. Here, we present a metal-organic framework (MOF)-derived cobalt-doped Fe@Fe2O3 (Co-Fe@Fe2O3) NO3-RR catalyst for electrochemical energy production. This catalyst has a nitrate removal capacity of 100.8 mg N gcat-1 h-1 and an ammonium selectivity of 99.0 ± 0.1%, which was the highest among all reported research. In addition, NH3 was produced at a rate of 1,505.9 µg h-1 cm-2, and the maximum faradaic efficiency was 85.2 ± 0.6%. Experimental and computational results reveal that the high performance of Co-Fe@Fe2O3 results from cobalt doping, which tunes the Fe d-band center, enabling the adsorption energies for intermediates to be modulated and suppressing hydrogen production. Thus, this study provides a strategy in the design of electrocatalysts in electrochemical nitrate reduction.

Boosted ammonium production by single cobalt atom catalysts with high Faradic efficiencies.

Efficient n = O bond activation is crucial for the catalytic reduction of nitrogen compounds, which is highly affected by the construction of active centers. In this study, n = O bond activation was achieved by a single-atom catalyst (SAC) with phosphorus anchored on a Co active center to form intermediate N-species for further hydrogenation and reduction. Unique phosphorus-doped discontinuous active sites exhibit better n = O activation performance than conventional N-cooperated single-atom sites, with a high Faradic efficiency of 92.0% and a maximum ammonia yield rate of 433.3 µg NH4·h-1·cm-2. This approach of constructing environmental sites through heteroatom modification significantly improves atom efficiency and will guide the design of future functional SACs with wide-ranging applications.

Structural basis for replicase polyprotein cleavage and substrate specificity of main protease from SARS-CoV-2.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key enzyme, which extensively digests CoV replicase polyproteins essential for viral replication and transcription, making it an attractive target for antiviral drug development. However, the molecular mechanism of how Mpro of SARS-CoV-2 digests replicase polyproteins, releasing the nonstructural proteins (nsps), and its substrate specificity remain largely unknown. Here, we determine the high-resolution structures of SARS-CoV-2 Mpro in its resting state, precleavage state, and postcleavage state, constituting a full cycle of substrate cleavage. The structures show the delicate conformational changes that occur during polyprotein processing. Further, we solve the structures of the SARS-CoV-2 Mpro mutant (H41A) in complex with six native cleavage substrates from replicase polyproteins, and demonstrate that SARS-CoV-2 Mpro can recognize sequences as long as 10 residues but only have special selectivity for four subsites. These structural data provide a basis to develop potent new inhibitors against SARS-CoV-2.