Twisted-layer boron nitride ceramic with high deformability and strength.

Moiré superlattices formed by twisted stacking in van der Waals materials have emerged as a new platform for exploring the physics of strongly correlated materials and other emergent phenomena1-5. However, there remains a lack of research on the mechanical properties of twisted-layer van der Waals materials, owing to a lack of suitable strategies for making three-dimensional bulk materials. Here we report the successful synthesis of a polycrystalline boron nitride bulk ceramic with high room-temperature deformability and strength. This ceramic, synthesized from an onion-like boron nitride nanoprecursor with conventional spark plasma sintering and hot-pressing sintering, consists of interlocked laminated nanoplates in which parallel laminae are stacked with varying twist angles. The compressive strain of this bulk ceramic can reach 14% before fracture, about one order of magnitude higher compared with traditional ceramics (less than 1% in general), whereas the compressive strength is about six times that of ordinary hexagonal boron nitride layered ceramics. The exceptional mechanical properties are due to a combination of the elevated intrinsic deformability of the twisted layering in the nanoplates and the three-dimensional interlocked architecture that restricts deformation from propagating across individual nanoplates. The advent of this twisted-layer boron nitride bulk ceramic opens a gate to the fabrication of highly deformable bulk ceramics.

Non-Hermitian topological whispering gallery.

In 1878, Lord Rayleigh observed the highly celebrated phenomenon of sound waves that creep around the curved gallery of St Paul's Cathedral in London1,2. These whispering-gallery waves scatter efficiently with little diffraction around an enclosure and have since found applications in ultrasonic fatigue and crack testing, and in the optical sensing of nanoparticles or molecules using silica microscale toroids. Recently, intense research efforts have focused on exploring non-Hermitian systems with cleverly matched gain and loss, facilitating unidirectional invisibility and exotic characteristics of exceptional points3,4. Likewise, the surge in physics using topological insulators comprising non-trivial symmetry-protected phases has laid the groundwork in reshaping highly unconventional avenues for robust and reflection-free guiding and steering of both sound and light5,6. Here we construct a topological gallery insulator using sonic crystals made of thermoplastic rods that are decorated with carbon nanotube films, which act as a sonic gain medium by virtue of electro-thermoacoustic coupling. By engineering specific non-Hermiticity textures to the activated rods, we are able to break the chiral symmetry of the whispering-gallery modes, which enables the out-coupling of topological 'audio lasing' modes with the desired handedness. We foresee that these findings will stimulate progress in non-destructive testing and acoustic sensing.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

MLSNet: a deep learning model for predicting transcription factor binding sites.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet.

Compression of curium pyrrolidine-dithiocarbamate enhances covalency.

Curium is unique in the actinide series because its half-filled 5f 7 shell has lower energy than other 5f n configurations, rendering it both redox-inactive and resistant to forming chemical bonds that engage the 5f shell1-3. This is even more pronounced in gadolinium, curium's lanthanide analogue, owing to the contraction of the 4f orbitals with respect to the 5f orbitals4. However, at high pressures metallic curium undergoes a transition from localized to itinerant 5f electrons5. This transition is accompanied by a crystal structure dictated by the magnetic interactions between curium atoms5,6. Therefore, the question arises of whether the frontier metal orbitals in curium(III)-ligand interactions can also be modified by applying pressure, and thus be induced to form metal-ligand bonds with a degree of covalency. Here we report experimental and computational evidence for changes in the relative roles of the 5f/6d orbitals in curium-sulfur bonds in [Cm(pydtc)4]- (pydtc, pyrrolidinedithiocarbamate) at high pressures (up to 11 gigapascals). We compare these results to the spectra of [Nd(pydtc)4]- and of a Cm(III) mellitate that possesses only curium-oxygen bonds. Compared with the changes observed in the [Cm(pydtc)4]- spectra, we observe smaller changes in the f-f transitions in the [Nd(pydtc)4]- absorption spectrum and in the f-f emission spectrum of the Cm(III) mellitate upon pressurization, which are related to the smaller perturbation of the nature of their bonds. These results reveal that the metal orbital contributions to the curium-sulfur bonds are considerably enhanced at high pressures and that the 5f orbital involvement doubles between 0 and 11 gigapascal. Our work implies that covalency in actinides is complex even when dealing with the same ion, but it could guide the selection of ligands to study the effect of pressure on actinide compounds.

Colloidal tubular microrobots for cargo transport and compression.

Microrobot swarms have seen increased interest in recent years due to their potentials for in vivo delivery and imaging with cooperative propulsion modes and enhanced imaging signals. Yet most swarms developed so far are limited to dense particle aggregates, far simpler than complicated three-dimensional assemblies of anisotropic particles. Here, we show via assembly path design that complex hollow tubular structures can be assembled from simple isotropic colloidal spheres and those complicated, metastable, microtubes can be formed from simple, energetically favorable colloidal membranes. The assembled microtubes can remain intact and roll under a precessing magnetic field, with propulsion directions and velocities precisely controlled by field components. The hollow spaces inside enable these tubular microrobots to grab, transport, and release cargos on command. We also demonstrate unique compressing and uncompressing capabilities with our tubular microrobots, making them effective microtweezers. Our work shows that complicated microrobots can be transformed from simple assemblies, providing an insight on building micromachines.

Unusual magnetotransport in twisted bilayer graphene from strain-induced open Fermi surfaces.

Anisotropic hopping in a toy Hofstadter model was recently invoked to explain a rich and surprising Landau spectrum measured in twisted bilayer graphene away from the magic angle. Suspecting that such anisotropy could arise from unintended uniaxial strain, we extend the Bistritzer-MacDonald model to include uniaxial heterostrain and present a detailed analysis of its impact on band structure and magnetotransport. We find that such strain strongly influences band structure, shifting the three otherwise-degenerate van Hove points to different energies. Coupled to a Boltzmann magnetotransport calculation, this reproduces previously unexplained nonsaturating [Formula: see text] magnetoresistance over broad ranges of density near filling [Formula: see text] and predicts subtler features that had not been noticed in the experimental data. In contrast to these distinctive signatures in longitudinal resistivity, the Hall coefficient is barely influenced by strain, to the extent that it still shows a single sign change on each side of the charge neutrality point-surprisingly, this sign change no longer occurs at a van Hove point. The theory also predicts a marked rotation of the electrical transport principal axes as a function of filling even for fixed strain and for rigid bands. More careful examination of interaction-induced nematic order versus strain effects in twisted bilayer graphene could thus be in order.

mitoSplitter: A mitochondrial variants-based method for efficient demultiplexing of pooled single-cell RNA-seq.

Single-cell RNA-seq (scRNA-seq) analysis of multiple samples separately can be costly and lead to batch effects. Exogenous barcodes or genome-wide RNA mutations can be used to demultiplex pooled scRNA-seq data, but they are experimentally or computationally challenging and limited in scope. Mitochondrial genomes are small but diverse, providing concise genotype information. We developed "mitoSplitter," an algorithm that demultiplexes samples using mitochondrial RNA (mtRNA) variants, and demonstrated that mtRNA variants can be used to demultiplex large-scale scRNA-seq data. Using affordable computational resources, mitoSplitter can accurately analyze 10 samples and 60,000 cells in 6 h. To avoid the batch effects from separated experiments, we applied mitoSplitter to analyze the responses of five non-small cell lung cancer cell lines to BET (Bromodomain and extraterminal) chemical degradation in a multiplexed fashion. We found the synthetic lethality of TOP2A inhibition and BET chemical degradation in BET inhibitor-resistant cells. The result indicates that mitoSplitter can accelerate the application of scRNA-seq assays in biomedical research.

A new capacity of gut microbiota: Fermentation of engineered inorganic carbon nanomaterials into endogenous organic metabolites.

Carbon-based nanomaterials (CNMs) have recently been found in humans raising a great concern over their adverse roles in the hosts. However, our knowledge of the in vivo behavior and fate of CNMs, especially their biological processes elicited by the gut microbiota, remains poor. Here, we uncovered the integration of CNMs (single-walled carbon nanotubes and graphene oxide) into the endogenous carbon flow through degradation and fermentation, mediated by the gut microbiota of mice using isotope tracing and gene sequencing. As a newly available carbon source for the gut microbiota, microbial fermentation leads to the incorporation of inorganic carbon from the CNMs into organic butyrate through the pyruvate pathway. Furthermore, the butyrate-producing bacteria are identified to show a preference for the CNMs as their favorable source, and excessive butyrate derived from microbial CNMs fermentation further impacts on the function (proliferation and differentiation) of intestinal stem cells in mouse and intestinal organoid models. Collectively, our results unlock the unknown fermentation processes of CNMs in the gut of hosts and underscore an urgent need for assessing the transformation of CNMs and their health risk via the gut-centric physiological and anatomical pathways.

The high-energy transition state of the glutamate transporter homologue GltPh.

Membrane transporters mediate cellular uptake of nutrients, signaling molecules, and drugs. Their overall mechanisms are often well understood, but the structural features setting their rates are mostly unknown. Earlier single-molecule fluorescence imaging of the archaeal model glutamate transporter homologue GltPh from Pyrococcus horikoshii suggested that the slow conformational transition from the outward- to the inward-facing state, when the bound substrate is translocated from the extracellular to the cytoplasmic side of the membrane, is rate limiting to transport. Here, we provide insight into the structure of the high-energy transition state of GltPh that limits the rate of the substrate translocation process. Using bioinformatics, we identified GltPh gain-of-function mutations in the flexible helical hairpin domain HP2 and applied linear free energy relationship analysis to infer that the transition state structurally resembles the inward-facing conformation. Based on these analyses, we propose an approach to search for allosteric modulators for transporters.

TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters.

BACKGROUND: Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essential for synthesizing the gene-encoded products by bacteria to grow and adapt to different environmental conditions. A variety of machine learning-based predictors for bacterial promoters have been developed; however, most of them were designed specifically for a particular species. To date, only a few predictors are available for identifying general bacterial promoters with limited predictive performance. RESULTS: In this study, we developed TIMER, a Siamese neural network-based approach for identifying both general and species-specific bacterial promoters. Specifically, TIMER uses DNA sequences as the input and employs three Siamese neural networks with the attention layers to train and optimize the models for a total of 13 species-specific and general bacterial promoters. Extensive 10-fold cross-validation and independent tests demonstrated that TIMER achieves a competitive performance and outperforms several existing methods on both general and species-specific promoter prediction. As an implementation of the proposed method, the web server of TIMER is publicly accessible at http://web.unimelb-bioinfortools.cloud.edu.au/TIMER/.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

A framework to improve the alignment of individual cytoarchitectonic maps of the Julich-Brain atlas using cortical folding landmarks.

The segregation of the cortical mantle into cytoarchitectonic areas provides a structural basis for the specialization of different brain regions. In vivo neuroimaging experiments can be linked to this postmortem cytoarchitectonic parcellation via Julich-Brain. This atlas embeds probabilistic maps that account for inter-individual variability in the localization of cytoarchitectonic areas in the reference spaces targeted by spatial normalization. We built a framework to improve the alignment of architectural areas across brains using cortical folding landmarks. This framework, initially designed for in vivo imaging, was adapted to postmortem histological data. We applied this to the first 14 brains used to establish the Julich-Brain atlas to infer a refined atlas with more focal probabilistic maps. The improvement achieved is significant in the primary regions and some of the associative areas. This framework also provides a tool for exploring the relationship between cortical folding patterns and cytoarchitectonic areas in different cortical regions to establish new landmarks in the remainder of the cortex.

The influence of CLEC5A on early macrophage-mediated inflammation in COPD progression.

Chronic obstructive pulmonary disease (COPD) is a complex syndrome with poorly understood mechanisms driving its early progression (GOLD stages 1-2). Elucidating the genetic factors that influence early-stage COPD, particularly those related to airway inflammation and remodeling, is crucial. This study analyzed lung tissue sequencing data from patients with early-stage COPD (GSE47460) and smoke-exposed mice. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning to identify potentially pathogenic genes. Further analyses included single-cell sequencing from both mice and COPD patients to pinpoint gene expression in specific cell types. Cell-cell communication and pseudotemporal analyses were conducted, with findings validated in smoke-exposed mice. Additionally, Mendelian randomization (MR) was used to confirm the association between candidate genes and lung function/COPD. Finally, functional validation was performed in vitro using cell cultures. Machine learning analysis of 30 differentially expressed genes identified 8 key genes, with CLEC5A emerging as a potential pathogenic factor in early-stage COPD. Bioinformatics analyses suggested a role for CLEC5A in macrophage-mediated inflammation during COPD. Two-sample Mendelian randomization linked CLEC5A single nucleotide polymorphisms (SNPs) with Forced Expiratory Volume in One Second (FEV1), FEV1/Forced Vital Capacity (FVC) and early/later on COPD. In vitro, the knockdown of CLEC5A led to a reduction in inflammatory markers within macrophages. Our study identifies CLEC5A as a critical gene in early-stage COPD, contributing to its pathogenesis through pro-inflammatory mechanisms. This discovery offers valuable insights for developing early diagnosis and treatment strategies for COPD and highlights CLEC5A as a promising target for further investigation.

Nanozyme-Enhanced Probiotic Spores Regulate the Intestinal Microenvironment for Targeted Acute Gastroenteritis Therapy.

Antibiotic therapeutics to combat intestinal pathogen infections often exacerbate microbiota dysbiosis and impair mucosal barrier functions. Probiotics are promising strategies, because they inhibit pathogen colonization and improve intestinal microbiota imbalance. Nevertheless, their limited targeting ability and susceptibility to oxidative stress have hindered their therapeutic potential. To tackle these challenges, Ces3 is synthesized by in situ growth of CeO2 nanozymes with positive charges on probiotic spores, facilitating electrostatic interactions with negatively charged pathogens and possessing a high reactive oxygen species (ROS) scavenging activity. Importantly, Ces3 can resist the harsh environment of the gastrointestinal tract. In mice with S. Typhimurium-infected acute gastroenteritis, Ces3 shows potent anti-S. Typhimurium activity, thereby alleviating the dissemination of S. Typhimurium into other organs. Additionally, owing to its O2 deprivation capacity, Ces3 promotes the proliferation of anaerobic probiotics, reshaping a healthy intestinal microbiota. This work demonstrates the promise of combining antibacterial, anti-inflammatory, and O2 content regulation properties for acute gastroenteritis therapy.

Arabidopsis SMO2 modulates ribosome biogenesis by maintaining the RID2 abundance during organ growth.

Ribosome biogenesis is a process of making ribosomes that is tightly linked with plant growth and development. Here, through a suppressor screen for the smo2 mutant, we found that lack of a ribosomal stress response mediator, ANAC082 partially restored growth defects of the smo2 mutant, indicating SMO2 is required for the repression of nucleolar stress. Consistently, the smo2 knock-out mutant exhibited typical phenotypes characteristic of ribosome biogenesis mutants, such as pointed leaves, aberrant leaf venation, disrupted nucleolar structure, abnormal distribution of rRNA precursors, and enhanced tolerance to aminoglycoside antibiotics that target ribosomes. SMO2 interacted with ROOT INITIATION DEFECTIVE 2 (RID2), a methyltransferase-like protein required for pre-rRNA processing. SMO2 enhanced RID2 solubility in Escherichia coli and the loss of function of SMO2 in plant cells reduced RID2 abundance, which may result in abnormal accumulation of FIBRILLARIN 1 (FIB1) and NOP56, two key nucleolar proteins, in high-molecular-weight protein complex. Taken together, our results characterized a novel plant ribosome biogenesis factor, SMO2 that maintains the abundance of RID2, thereby sustaining ribosome biogenesis during plant organ growth.

Jmjd4 Facilitates Pkm2 Degradation in Cardiomyocytes and Is Protective Against Dilated Cardiomyopathy.

BACKGROUND: A large portion of idiopathic and familial dilated cardiomyopathy (DCM) cases have no obvious causal genetic variant. Although altered response to metabolic stress has been implicated, the molecular mechanisms underlying the pathogenesis of DCM remain elusive. The JMJD family proteins, initially identified as histone deacetylases, have been shown to be involved in many cardiovascular diseases. Despite their increasingly diverse functions, whether JMJD family members play a role in DCM remains unclear. METHODS: We examined Jmjd4 expression in patients with DCM, and conditionally deleted and overexpressed Jmjd4 in cardiomyocytes in vivo to investigate its role in DCM. RNA sequencing, metabolites profiling, and mass spectrometry were used to dissect the molecular mechanism of Jmjd4-regulating cardiac metabolism and hypertrophy. RESULTS: We found that expression of Jmjd4 is significantly decreased in hearts of patients with DCM. Induced cardiomyocyte-specific deletion of Jmjd4 led to spontaneous DCM with severely impaired mitochondrial respiration. Pkm2, the less active pyruvate kinase compared with Pkm1, which is normally absent in healthy adult cardiomyocytes but elevated in cardiomyopathy, was found to be drastically accumulated in hearts with Jmjd4 deleted. Jmjd4 was found mechanistically to interact with Hsp70 to mediate degradation of Pkm2 through chaperone-mediated autophagy, which is dependent on hydroxylation of K66 of Pkm2 by Jmjd4. By enhancing the enzymatic activity of the abundant but less active Pkm2, TEPP-46, a Pkm2 agonist, showed a significant therapeutic effect on DCM induced by Jmjd4 deficiency, and heart failure induced by pressure overload, as well. CONCLUSIONS: Our results identified a novel role of Jmjd4 in maintaining metabolic homeostasis in adult cardiomyocytes by degrading Pkm2 and suggest that Jmjd4 and Pkm2 may be therapeutically targeted to treat DCM, and other cardiac diseases with metabolic dysfunction, as well.

Comprehensive genomic analysis of the SARS-CoV-2 Omicron variant BA.2.76 in Jining City, China, 2022.

OBJECTIVE: This study aims to analyze the molecular characteristics of the novel coronavirus (SARS-CoV-2) Omicron variant BA.2.76 in Jining City, China. METHODS: Whole-genome sequencing was performed on 87 cases of SARS-CoV-2 infection. Evolutionary trees were constructed using bioinformatics software to analyze sequence homology, variant sites, N-glycosylation sites, and phosphorylation sites. RESULTS: All 87 SARS-CoV-2 whole-genome sequences were classified under the evolutionary branch of the Omicron variant BA.2.76. Their similarity to the reference strain Wuhan-Hu-1 ranged from 99.72 to 99.74%. In comparison to the reference strain Wuhan-Hu-1, the 87 sequences exhibited 77-84 nucleotide differences and 27 nucleotide deletions. A total of 69 amino acid variant sites, 9 amino acid deletions, and 1 stop codon mutation were identified across 18 proteins. Among them, the spike (S) protein exhibited the highest number of variant sites, and the ORF8 protein showed a Q27 stop mutation. Multiple proteins displayed variations in glycosylation and phosphorylation sites. CONCLUSION: SARS-CoV-2 continues to evolve, giving rise to new strains with enhanced transmission, stronger immune evasion capabilities, and reduced pathogenicity. The application of high-throughput sequencing technologies in the epidemic prevention and control of COVID-19 provides crucial insights into the evolutionary and variant characteristics of the virus at the genomic level, thereby holding significant implications for the prevention and control of the COVID-19 pandemic.

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production.

Self-assembling peptides represent a captivating area of study in nanotechnology and biomaterials. This interest is largely driven by their unique properties and the vast application potential across various fields such as catalytic functions. However, design complexities, including high-dimensional sequence space and structural diversity, pose significant challenges in the study of such systems. In this work, we explored the possibility of self-assembled peptides to catalyze the hydrolysis of hydrosilane for hydrogen production using ab initio calculations and carried out wet-lab experiments to confirm the feasibility of these catalytic reactions under ambient conditions. Further, we delved into the nuanced interplay between sequence, structural conformation, and catalytic activity by combining modeling with experimental techniques such as transmission electron microscopy and nuclear magnetic resonance and proposed a dual mode of the microstructure of the catalytic center. Our results reveal that although research in this area is still at an early stage, the development of self-assembled peptide catalysts for hydrogen production has the potential to provide a more sustainable and efficient alternative to conventional hydrogen production methods. In addition, this work also demonstrates that a computation-driven rational design supplemented by experimental validation is an effective protocol for conducting research on functional self-assembled peptides.

Asymmetric Intramolecular Amination Catalyzed with Cp*Ir-SPDO via Nitrene Transfer for Synthesis of Spiro-Quaternary Indolinone.

An asymmetric intramolecular spiro-amination to high steric hindering α-C-H bond of 1,3-dicarbonyl via nitrene transfer using inactive aryl azides has been carried out by developing a novel Cp*Ir(III)-SPDO (spiro-pyrrolidine oxazoline) catalyst, thereby enabling the first successful construction of structurally rigid spiro-quaternary indolinone cores with moderate to high yields and excellent enantioselectivities. DFT computations support the presence of double bridging H-F bonds between [SbF6]- and both the ligand and substrate, which favors the plane-differentiation of the enol π-bond for nitrenoid attacking. These findings open up numerous opportunities for the development of new asymmetric nitrene transfer systems.