Thermus thermophilus Argonaute Functions in the Completion of DNA Replication.

In many eukaryotes, Argonaute proteins, guided by short RNA sequences, defend cells against transposons and viruses. In the eubacterium Thermus thermophilus, the DNA-guided Argonaute TtAgo defends against transformation by DNA plasmids. Here, we report that TtAgo also participates in DNA replication. In vivo, TtAgo binds 15- to 18-nt DNA guides derived from the chromosomal region where replication terminates and associates with proteins known to act in DNA replication. When gyrase, the sole T. thermophilus type II topoisomerase, is inhibited, TtAgo allows the bacterium to finish replicating its circular genome. In contrast, loss of gyrase and TtAgo activity slows growth and produces long sausage-like filaments in which the individual bacteria are linked by DNA. Finally, wild-type T. thermophilus outcompetes an otherwise isogenic strain lacking TtAgo. We propose that the primary role of TtAgo is to help T. thermophilus disentangle the catenated circular chromosomes generated by DNA replication.

Gasdermin D permeabilization of mitochondrial inner and outer membranes accelerates and enhances pyroptosis.

Gasdermin D (GSDMD)-activated inflammatory cell death (pyroptosis) causes mitochondrial damage, but its underlying mechanism and functional consequences are largely unknown. Here, we show that the N-terminal pore-forming GSDMD fragment (GSDMD-NT) rapidly damaged both inner and outer mitochondrial membranes (OMMs) leading to reduced mitochondrial numbers, mitophagy, ROS, loss of transmembrane potential, attenuated oxidative phosphorylation (OXPHOS), and release of mitochondrial proteins and DNA from the matrix and intermembrane space. Mitochondrial damage occurred as soon as GSDMD was cleaved prior to plasma membrane damage. Mitochondrial damage was independent of the B-cell lymphoma 2 family and depended on GSDMD-NT binding to cardiolipin. Canonical and noncanonical inflammasome activation of mitochondrial damage, pyroptosis, and inflammatory cytokine release were suppressed by genetic ablation of cardiolipin synthase (Crls1) or the scramblase (Plscr3) that transfers cardiolipin to the OMM. Phospholipid scramblase-3 (PLSCR3) deficiency in a tumor compromised pyroptosis-triggered anti-tumor immunity. Thus, mitochondrial damage plays a critical role in pyroptosis.

Methotrexate recognition by the human reduced folate carrier SLC19A1.

Folates are essential nutrients with important roles as cofactors in one-carbon transfer reactions, being heavily utilized in the synthesis of nucleic acids and the metabolism of amino acids during cell division1,2. Mammals lack de novo folate synthesis pathways and thus rely on folate uptake from the extracellular milieu3. The human reduced folate carrier (hRFC, also known as SLC19A1) is the major importer of folates into the cell1,3, as well as chemotherapeutic agents such as methotrexate4-6. As an anion exchanger, RFC couples the import of folates and antifolates to anion export across the cell membrane and it is a major determinant in methotrexate (antifolate) sensitivity, as genetic variants and its depletion result in drug resistance4-8. Despite its importance, the molecular basis of substrate specificity by hRFC remains unclear. Here we present cryo-electron microscopy structures of hRFC in the apo state and captured in complex with methotrexate. Combined with molecular dynamics simulations and functional experiments, our study uncovers key determinants of hRFC transport selectivity among folates and antifolate drugs while shedding light on important features of anion recognition by hRFC.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding.

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Chemokine guidance of central memory T cells is critical for antiviral recall responses in lymph nodes.

A defining feature of vertebrate immunity is the acquisition of immunological memory, which confers enhanced protection against pathogens by mechanisms that are incompletely understood. Here, we compared responses by virus-specific naive T cells (T(N)) and central memory T cells (T(CM)) to viral antigen challenge in lymph nodes (LNs). In steady-state LNs, both T cell subsets localized in the deep T cell area and interacted similarly with antigen-presenting dendritic cells. However, upon entry of lymph-borne virus, only T(CM) relocalized rapidly and efficiently toward the outermost LN regions in the medullary, interfollicular, and subcapsular areas where viral infection was initially confined. This rapid peripheralization was coordinated by a cascade of cytokines and chemokines, particularly ligands for T(CM)-expressed CXCR3. Consequently, in vivo recall responses to viral infection by CXCR3-deficient T(CM) were markedly compromised, indicating that early antigen detection afforded by intranodal chemokine guidance of T(CM) is essential for efficient antiviral memory.

Self-delivering, chemically modified CRISPR RNAs for AAV co-delivery and genome editing in vivo.

Guide RNAs offer programmability for CRISPR-Cas9 genome editing but also add challenges for delivery. Chemical modification, which has been key to the success of oligonucleotide therapeutics, can enhance the stability, distribution, cellular uptake, and safety of nucleic acids. Previously, we engineered heavily and fully modified SpyCas9 crRNA and tracrRNA, which showed enhanced stability and retained activity when delivered to cultured cells in the form of the ribonucleoprotein complex. In this study, we report that a short, fully stabilized oligonucleotide (a 'protecting oligo'), which can be displaced by tracrRNA annealing, can significantly enhance the potency and stability of a heavily modified crRNA. Furthermore, protecting oligos allow various bioconjugates to be appended, thereby improving cellular uptake and biodistribution of crRNA in vivo. Finally, we achieved in vivo genome editing in adult mouse liver and central nervous system via co-delivery of unformulated, chemically modified crRNAs with protecting oligos and AAV vectors that express tracrRNA and either SpyCas9 or a base editor derivative. Our proof-of-concept establishment of AAV/crRNA co-delivery offers a route towards transient editing activity, target multiplexing, guide redosing, and vector inactivation.

Structural and thermodynamic framework for PIEZO1 modulation by small molecules.

Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.

MOTIVATION: Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. RESULTS: In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The code implemented in Python and the data used for experiments have been released on GitHub (https://github.com/zhanglabNKU/BERMAD) and Zenodo (https://zenodo.org/records/10695073) with detailed instructions.

Surface Engineering of Two-Dimensional Black Phosphorus for Advanced Nanophotonics.

ConspectusEverything in the world has two sides. We should correctly understand its two sides to pursue the positive side and get rid of the negative side. Recently, two-dimensional (2D) black phosphorus (BP) has received a tremendous amount of attention and has been applied for broad applications in optoelectronics, transistors, logic devices, and biomedicines due to its intrinsic properties, e.g., thickness-dependent bandgap, high mobility, highly anisotropic charge transport, and excellent biodegradability and biocompatibility. On one hand, rapid degradation of 2D BP under ambient conditions becomes a vital bottleneck that largely hampers its practical applications in optical and optoelectronic devices and photocatalysis. On the other hand, just because of its ambient instability, 2D BP as a novel kind of nanomedicine in a cancer drug delivery system can not only satisfy effective cancer therapy but also enable its safe biodegradation in vivo. Until now, a variety of surface functionality types and approaches have been employed to rationally modify 2D BP to meet the growing requirements of advanced nanophotonics.In this Account, we describe our research on surface engineering of 2D BP in two opposite ways: (i) stabilizing 2D BP by various approaches for advanced nanophotonic devices with both remarkable photoresponse behavior and environmentally structural stability and (ii) making full use of biodegradation, biocompatibility, and biological activity (e.g., photothermal therapy, photodynamic therapy, and bioimaging) of 2D BP for the construction of high-performance delivery nanoplatforms for biophotonic applications. We highlight the targeted aims of the surface-engineered 2D BP for advanced nanophotonics, including photonic devices (optics, optoelectronics, and photocatalysis) and photoinduced cancer therapy, by means of various surface functionalities, such as heteroatom incorporation, polymer functionalization, coating, heterostructure design, etc. From the viewpoint of potential applications, the fundamental properties of surface-engineered 2D BP and recent advances in surface-engineered 2D BP-based nanophotonic devices are briefly discussed. For the photonic devices, surface-engineered 2D BP can not only effectively improve environmentally structural stability but also simultaneously maintain photoresponse performance, enabling 2D BP-based devices for a wide range of practical applications. In terms of the photoinduced cancer therapy, surface-engineered 2D BP is more appropriate for the treatment of cancer due to negligible toxicity and excellent biodegradation and biocompatibility. We also provide our perspectives on future opportunities and challenges in this important and fast-growing field. It is envisioned that this Account can attract more attention in this area and inspire more scientists in a variety of research communities to accelerate the development of 2D BP for more widespread high-performance nanophotonic applications.

Language model based on deep learning network for biomedical named entity recognition.

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition. To solve the problem of polysemous words and insufficient data, for the task of biomedical named entity recognition, we propose a multi-task learning framework fused with language model based on the BiLSTM-CRF architecture. Our model uses a language model to design a differential encoding of the context, which could obtain dynamic word vectors to distinguish words in different datasets. Moreover, we use a multi-task learning method to collectively share the dynamic word vector of different types of entities to improve the recognition performance of each type of entity. Experimental results show that our model reduces the false positives caused by polysemous words through differentiated coding, and improves the performance of each subtask by sharing information between different entity data. Compared with other state-of-the art methods, our model achieved superior results in four typical training sets, and achieved the best results in F1 values.

Plant cell-surface GIPC sphingolipids sense salt to trigger Ca2+ influx.

Salinity is detrimental to plant growth, crop production and food security worldwide. Excess salt triggers increases in cytosolic Ca2+ concentration, which activate Ca2+-binding proteins and upregulate the Na+/H+ antiporter in order to remove Na+. Salt-induced increases in Ca2+ have long been thought to be involved in the detection of salt stress, but the molecular components of the sensing machinery remain unknown. Here, using Ca2+-imaging-based forward genetic screens, we isolated the Arabidopsis thaliana mutant monocation-induced [Ca2+]i increases 1 (moca1), and identified MOCA1 as a glucuronosyltransferase for glycosyl inositol phosphorylceramide (GIPC) sphingolipids in the plasma membrane. MOCA1 is required for salt-induced depolarization of the cell-surface potential, Ca2+ spikes and waves, Na+/H+ antiporter activation, and regulation of growth. Na+ binds to GIPCs to gate Ca2+ influx channels. This salt-sensing mechanism might imply that plasma-membrane lipids are involved in adaption to various environmental salt levels, and could be used to improve salt resistance in crops.

Fracture of model end-linked networks.

Advances in polymer chemistry over the last decade have enabled the synthesis of molecularly precise polymer networks that exhibit homogeneous structure. These precise polymer gels create the opportunity to establish true multiscale, molecular to macroscopic, relationships that define their elastic and failure properties. In this work, a theory of network fracture that accounts for loop defects is developed by drawing on recent advances in network elasticity. This loop-modified Lake-Thomas theory is tested against both molecular dynamics (MD) simulations and experimental fracture measurements on model gels, and good agreement between theory, which does not use an enhancement factor, and measurement is observed. Insight into the local and global contributions to energy dissipated during network failure and their relation to the bond dissociation energy is also provided. These findings enable a priori estimates of fracture energy in swollen gels where chain scission becomes an important failure mechanism.

PARP1 recruits DNA translocases to restrain DNA replication and facilitate DNA repair.

Replication fork reversal which restrains DNA replication progression is an important protective mechanism in response to replication stress. PARP1 is recruited to stalled forks to restrain DNA replication. However, PARP1 has no helicase activity, and the mechanism through which PARP1 participates in DNA replication restraint remains unclear. Here, we found novel protein-protein interactions between PARP1 and DNA translocases, including HLTF, SHPRH, ZRANB3, and SMARCAL1, with HLTF showing the strongest interaction among these DNA translocases. Although HLTF and SHPRH share structural and functional similarity, it remains unclear whether SHPRH contains DNA translocase activity. We further identified the ability of SHPRH to restrain DNA replication upon replication stress, indicating that SHPRH itself could be a DNA translocase or a helper to facilitate DNA translocation. Although hydroxyurea (HU) and MMS induce different types of replication stress, they both induce common DNA replication restraint mechanisms independent of intra-S phase activation. Our results suggest that the PARP1 facilitates DNA translocase recruitment to damaged forks, preventing fork collapse and facilitating DNA repair.

Interfacial Passivation Engineering for Highly Efficient Quantum Dot Light-Emitting Diodes via Aromatic Amine-Functionalized Dipole Molecules.

Blue quantum dot (QD) light-emitting diodes (QLEDs) exhibit unsatisfactory operational stability and electroluminescence (EL) properties due to severe nonradiative recombination induced by large numbers of dangling bond defects and charge imbalance in QD. Herein, dipolar aromatic amine-functionalized molecules with different molecular polarities are employed to regulate charge transport and passivate interfacial defects between QD and the electron transfer layer (ETL). The results show that the stronger the molecular polarity, especially with the -CF3 groups possessing a strong electron-withdrawing capacity, the more effective the defect passivation of S and Zn dangling bonds at the QD surface. Moreover, the dipole interlayer can effectively reduce electron injection into QD at high current density, enhancing charge balance and mitigating Joule heat. Finally, blue QLEDs exhibit a peak external quantum efficiency (EQE) of 21.02% with an operational lifetime (T50 at 100 cd m-2) exceeding 4000 h.

Comparative Proteomics Elucidates the Potential Mechanism of Sperm Capacitation of Chinese Mitten Crabs (Eriocheir sinensis).

Sperm capacitation is broadly defined as a suite of biochemical and biophysical changes resulting from the acquisition of fertilization ability. To gain insights into the regulation mechanism of crustacean sperm capacitation, 4D label-free quantitative proteomics was first applied to analyze the changes of sperm in Eriocheir sinensis under three sequential physiological conditions: seminal vesicles (X2), hatched with the seminal receptacle content (X3), and incubated with egg water (X5). In total, 1536 proteins were identified, among which 880 proteins were quantified, with 82 and 224 proteins significantly altered after incubation with the seminal receptacle contents and egg water. Most differentially expressed proteins were attributed to biological processes by Gene Ontology annotation analysis. As the fundamental bioenergetic metabolism of sperm, the oxidative phosphorylation, glycolysis, and the pentose phosphate pathway presented significant changes under the treatment of seminal receptacle contents, indicating intensive regulation for sperm in the seminal receptacle. Additionally, the seminal receptacle contents also significantly increased the oxidation level of sperm, whereas the enhancement of abundance in superoxide dismutase, peroxiredoxin 1, and glutathione S-transferase after incubation with egg water significantly improved the resistance against oxidation. These results provided a new perspective for reproduction studies in crustaceans.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

Coordination between two cis-elements of WRKY33, bound by the same transcription factor, confers humid adaption in Arabidopsis thaliana.

To cope with flooding-induced hypoxia, plants have evolved different strategies. Molecular strategies, such as the N-degron pathway and transcriptional regulation, are known to be crucial for Arabidopsis thaliana's hypoxia response. Our study uncovered a novel molecular strategy that involves a single transcription factor interacting with two identical cis-elements, one located in the promoter region and the other within the intron. This unique double-element adjustment mechanism has seldom been reported in previous studies. In humid areas, WRKY70 plays a crucial role in A. thaliana's adaptation to submergence-induced hypoxia by binding to identical cis-elements in both the promoter and intron regions of WRKY33. This dual binding enhances WRKY33 expression and the activation of hypoxia-related genes. Conversely, in arid regions lacking the promoter cis-element, WRKY70 only binds to the intron cis-element, resulting in limited WRKY33 expression during submergence stress. The presence of a critical promoter cis-element in humid accessions, but not in dry accessions, indicates a coordinated regulation enabling A. thaliana to adapt and thrive in humid habitats.

Metal-Organic Frameworks-Based Frustrated Lewis Pairs for Selective Reduction of Nitroolefins to Nitroalkanes.

Nitroalkanes serve as essential intermediates in the synthesis of pharmaceuticals, agrochemicals, and functional materials. To date, nitroalkanes are mainly prepared from homogeneous catalysts such as noble transition metal catalysts with poor recyclability. Herein, we propose a metal-organic framework-frustrated Lewis pair (MOF-FLP) heterogeneous catalyst for selectively reducing nitroolefins to nitroalkanes under moderate reaction conditions. MOF enrichment effect can significantly improve the catalytic efficiency compared to homogeneous FLP catalysts. Benefiting from the strong interaction between FLP and MOF, the MOF-FLP catalyst exhibits outstanding recyclability. This work not only provides a convenient route for nitroalkane synthesis but also showcases the potential of porous heterogeneous FLP catalysts, offering inspiration for future catalytic design strategies.

Pyroptotic macrophages promote proliferation and chemotherapy resistance of peripheral T-cell lymphoma via TLR4 signaling pathway.

Peripheral T-cell lymphoma (PTCL) is a highly aggressive type of non-Hodgkin's lymphoma with a poor prognosis. Pyroptosis is a newly discovered procedural cell death mode, which has been implicated to occur in both tumor cells and immune cells. However, the occurrence and effect of pyroptosis on PTCL remain unclear. Here, we found that pyroptosis occurred in interstitial macrophages of PTCL rather than in tumor cells. In clinical specimens, macrophage pyroptosis was associated with a poor prognosis of PTCL. In vitro experiments and gene sequencing results showed that pyroptotic macrophages could upregulate the expression of TLR4 through secreting inflammatory cytokines IL-18. Upregulated TLR4 activated its downstream NF-κB anti-apoptotic signaling pathway, thus leading to malignant proliferation and chemotherapy resistance of tumor cells. Moreover, the expression of factors such as XIAP in the NF-κB anti-apoptotic pathway was downregulated after the knockdown of TLR4, and the malignant promotion effect of pyroptotic macrophages on PTCL cells was also reversed. Our findings revealed the mechanism of pyroptotic macrophages promoting the malignant biological behavior of PTCL and elucidated the key role of TLR4 in this process. In-depth analysis of this mechanism will contribute to understanding the regulatory effect of PTCL by the tumor microenvironment and providing new ideas for the clinical treatment of PTCL.